text:
\section{Introduction}

Deep learning-based methods have recently achieved promising results on various medical image processing tasks, such as detection \cite{liu2017detecting, yan2019mulan} and segmentation \cite{ronneberger2015u, dou20173d}. Indeed, deep learning approaches can generalize effectively when the training and testing images are from the same modality (i.e., same distribution), approaching or surpassing human-level performance.

However, some researchers \cite{kalogeiton2016analysing, tommasi2016learning} have shown that well-trained models do not perform well when the testing images come from a different statistical distribution from the training images. This domain shift problem is common in real-world medical diagnosis since medical images at various steps of the clinical procedure are often obtained with different physical properties \cite{dou2018unsupervised}. For instance, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) play complementary roles in cardiac disease diagnosis while also exhibiting different appearances (See Fig. \ref{Showcase}). That difference post challenges for analyzing the MRI and CT images in clinical diagnosis.

One plausible solution is to obtain manual annotations for both the MRI and the CT images from medical experts. However, such a procedure is prohibitively time-consuming. (e.g., manual cardiac operations from MRI/CT consumes 2-4 hours \cite{zhuang2013challenges}). Unsupervised Domain adaptation (UDA), which automatically transfers knowledge from the source domain to the target domain (e.g., MRI to CT) without paired images, is an interesting idea.

For UDA with medical image segmentation, the source medical image with the ground truth segmentation is denoted as the source domain, whereas the target medical image without the ground truth segmentation is referred to as the target domain. Generally, the reported works such as \cite{dou2018unsupervised, dou2019pnp} align the source domain and the target domain by learning the domain-invariant features and minimizing the domain-specific discrepancies.

\begin{figure}[t]
    \centering
    \includegraphics[width=1.55cm]{Image_CT/CT.jpg}
    \includegraphics[width=1.7cm]{Image_CT/MRI.jpg}
    \includegraphics[width=1.575cm]{Image_CT/PET.jpg}
    \includegraphics[width=1.7cm]{Image_CT/US.jpg}
    \caption{Four types of Cardiac Imaging. From left to right: Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Ultrasound (US).\cite{MRICT} Each type has significant difference with others in terms of color, contrast, structure, artifacts, and the edge information.}
    \label{Showcase}
\end{figure}

One popular research direction is to combine UDA with a GAN-based strategy, as GAN \cite{goodfellow2014generative} and its derivatives \cite{zhu2017unpaired, isola2017image} have exhibited remarkable unsupervised domain adaption ability. In the GAN-based UDA approach, the domain-invariant latent space features can be implicitly learned via adversarial learning during the min-max game between the generator and the discriminator. GAN-based approaches \cite{zhang2018translating, chen2020unsupervised, liu2020duda} have recently gained widespread acceptance in medical image analysis, outperforming prior Convolutional Neural Network (CNN) methods such as \cite{dou2018unsupervised, dou2019pnp} on cardiac segmentation tasks \cite{zhuang2016multi, zhang2018translating}. However, when we have a dataset (e.g., \cite{zhuang2018multivariate}) with extremely diverse imaging modalities and scanning methods, GAN-based approaches often fail to converge to the Nash Equilibrium \cite{heusel2017gans, karras2019style, karras2020analyzing, wu2021unsupervised}.

Recently, researchers \cite{ouyang2019data, gu2022few, wu2020cf, wu2021unsupervised} in UDA with medical imaging have turned to Variational Autoencoder (VAE) \cite{kingma2013auto} as the backbone due to their training stability at domain adaptation tasks at diverse imaging modalities \cite{purushotham2016variational, wu2021unsupervised} and their ability to handle scarce data in the target domain \cite{ouyang2019data, gu2022few}. These VAE-based methods usually perform posterior inference for the latent space variables using the normal distribution. That property allows it to consistently bridge two domains (i.e., source and target domain) towards standard and parameterized latent space variables \cite{wu2021unsupervised}.

Despite VAE-based methods' excellent domain adaptation ability at challenging benchmark cardiac segmentation datasets (e.g., \cite{zhuang2018multivariate}), two crucial factors restrain their learning capability. Firstly, VAE-based methods like UDA-VAE \cite{wu2021unsupervised} introduce a separate image reconstruction stage, aiming to regularize the latent space towards normal distribution. Although this strategy could explicitly minimize the domain discrepancy, the information from the reconstructed output cannot be directly delivered to the segmentation. Secondly, VAE-based approaches like CFDNet \cite{wu2020cf} utilize parallel reparameterization for latent space with different resolutions. The separation of low-resolution latent space and high-resolution latent space in U-Net-like architecture will potentially exaggerate the domain shift problem \cite{yan2019domain} and, therefore, degrade the model performance.

In this work, we propose a new framework, dubbed UDA-VAE++, that can well address unsupervised domain adaptation in cardiac image segmentation with diverse imaging modalities. Firstly, we leverage a U-Net backbone to extract the multi-scale features from unpaired images from the source and the target domain. The output at each encoder stair enters variational reasoning, followed by our sequential reparametrization design. That sequential design enables the network to transfer knowledge from low-resolution latent space to high-resolution latent space and constrains the encoded output according to standard normal distribution. A segmentation block follows the reparametrization operation at each level, and the segmentation output will be passed into a reconstruction block. Finally, we conduct mutual information (global, local, prior) estimation and maximization for the segmentation output and the reconstruction to evaluate the compact loss function lower bound.

The main contributions of this paper are highlighted as follows:
\begin{itemize}

    \item We deduce a compact loss function lower bound in which each term is orthogonal, discovering a new mutual information term.

    \item We design a novel, plug-and-play style, Structure Mutual Information Estimation (SMIE) block. This design enables an efficient mutual information estimate for the reconstruction output and the segmentation output, making the reconstruction and segmentation tasks mutually beneficial.

    \item We convert parallel reparameterization to sequential reparameterization, allowing information flow and variance correction from the low-resolution latent space to the high-resolution latent space after variational reasoning.

    \item We conduct extensive experiments to demonstrate that the proposed method surpasses previous state-of-the-arts on benchmark cardiac segmentation datasets qualitatively and quantitatively.
\end{itemize}

\section{Related Work}

\subsection{Unsupervised Domain Adaptation}

Unsupervised Domain Adaptation (UDA) has been widely used for biomedical image segmentation tasks. The early works \cite{dou2018unsupervised} and \cite{dou2019pnp} leverage unsupervised domain adaptation with adversarial training for multi-modal biomedical image segmentation. Specifically, both papers utilize a plug-and-play domain adaptation module to align the features in the source and the target domain.

Due to the promising generalization ability of Generative Adversarial Network (GAN) \cite{goodfellow2014generative}, recent research has begun to incorporate GAN in UDA for biomedical image segmentation. For example, \cite{zhang2018translating} utilizes CycleGAN \cite{zhu2017unpaired} with a shape-consistency loss to realize cross-domain translation between CT and MRI images. SIFA \cite{chen2020unsupervised} presents a synergistic domain alignment at both image-level and feature-level using the adversarial learning of CycleGAN to exploit domain-invariant characteristics. DUDA \cite{liu2020duda} further incorporates a cross-domain consistency loss to improve the segmentation performances.

Another faithful research direction is to use Variational Autoencoder (VAE) \cite{kingma2013auto}. That strategy is advantageous when there are few images in the target domain. For instance, \cite{ouyang2019data} follows the few-shot learning strategy, integrating a VAE-based feature prior to matching with adversarial learning to exploit the domain-invariant features. FUDA \cite{gu2022few} further incorporates Random Adaptive Instance Normalization to explore diverse target styles where there is only one unlabeled image in the target domain. The recent work CFDNet \cite{wu2020cf} proposes an effective metric, dubbed CF Distance, which enables explicit domain adaptation with image reconstruction and prior distribution matching. Another work UDA-VAE \cite{wu2021unsupervised} goes even further: it drives the latent space of the source and target domains towards a common, parameterized variational form following Gaussian Distribution.

Compared with previous UDA approaches, our method is the first that sequentially integrates multi-scale latent space features. That design enables our network to effectively minimize the domain-specific discrepancy according to the information flow from the low-resolution latent space to the high-resolution latent space.

\subsection{Mutual Information Neural Estimation}
Mutual Information Neural Estimation (MINE) is first introduced in \cite{belghazi2018mutual}, where the author utilizes gradient descent algorithms over neural networks to approximate the mutual information between continuous random variables. Based upon MINE, Deep InfoMax (DIM) \cite{hjelm2018learning} explores unsupervised visual representation learning by maximizing the mutual information for the network input and the encoded output under statistical constrain. A recent work \cite{chen2020structure} utilizes MINE to address the domain shift problem in unsupervised domain adaptation. Specifically, that paper integrates network predictions and local features into global features by simultaneously maximizing the mutual information.

Recently, MINE has been applied in biomedical image processing tasks. For example, based on MINE, \cite{ting2020multiview} maximizes the mutual information between source and fused images from Multiview 3-D Echocardiography. \cite{snaauw2022mutual} tackle the challenging unsupervised multimodal brain image segmentation task by estimating the mutual information using a lightweight convolutional neural network.

Different from previous MINE approaches, our framework is the first that conducts mutual information estimation and maximization with both image reconstruction and image segmentation. Our unique design enables image reconstruction and image segmentation to be mutually beneficial during model learning.

\begin{table}[t]
\setlength{\tabcolsep}{1mm}{
\centering{
\begin{tabular}{llll}
\toprule[1.0pt]
 & Symbols & Description                                         &  \\ \hline
 & $S$     & Source domain &  \\
 & $T$     & Target domain &  \\
 & $z$     & Latent variable                                                &  \\
  & $x$     & Input image data point        &  \\
  & $p_{\theta}()$    & PDF of variables with parameter $\theta$ \\
 & $q_{\phi}()$      & Neural network with parameter $\phi$                                            &  \\
 & $D(\phi_{S},\phi_{T})$      & Domain distance between source and target
                            &  \\
 & $\hat{y}$      & Predicted segmentation

                            &  \\

 & $y$      & Ground truth segmentation

                            &  \\
 & $R_{S}$      & Reconstructed image in the source domain
                            &  \\
 & $R_{T}$      & Reconstructed image in the target domain
                            &  \\
 & $D_{KL}$     & KL Divergence &\\
 & $\epsilon$       & Reconstruction error &\\
 & $H$       & Entropy & \\
\bottomrule[1.0pt]

\end{tabular}}
}
\caption{Preliminary for Important Symbols}
\end{table}
\begin{figure*}[t]
    \centering
    \includegraphics[width=15cm]{Model/UDA-VAE.pdf}
    \caption{The Model Architecture of UDA-VAE++. The backbone of UDA-VAE++ is U-Net (blue boxes) with three scales of variational blocks. The green line refers to the concatenation of the segmentation output, whereas the orange line indicates the concatenation of the reconstruction output. The reconstruction blocks (red boxes) contain seven convolution layers. The grey box refers to the MI estimation block detailed in Fig. \ref{MINE}}
    \label{Preliminary}
\end{figure*}
\section{Methodology}
In this section, we will discuss our UDA-VAE++ workflow, explain the proposed structure mutual information estimation block, and display the loss functions.
\subsection{UDA-VAE++ Model Workflow}
\begin{figure}[t]
    \centering
    \includegraphics[width=10cm]{Model/STdomain.pdf}
    \caption{The workflow for Unsupervised Domain Adaptation. The image from the source and target domain will first be encoded in the shared parameters downsampling part of the U-Net backbone. Next, each scale output will go through the same segmentation network. In the source domain, the ground truth segmentation masks combining the variables in latent space will be reconstructed by the upsampling part of U-Net. The MI block will maximize the mutual information of the segmentation output and the reconstruction output.}
    \label{UDAworkflow}
\end{figure}

As shown in Fig. \ref{Preliminary}, we use U-Net \cite{ronneberger2015u} as our backbone due to its remarkable success in medical image segmentation. Firstly, The network performs four downsamplings. Each of the downsampling operations uses two convolutional layers. Secondly, the network uses upsampling symmetrically with skip connection. We then obtain a multi-scale encoding output with channels of 256, 128, 64, and image sizes of 40$\times$40, 80$\times$80, 160$\times$160, respectively. Each encoding output will be followed by variational reasoning \cite{kingma2013auto, kingma2019introduction}. Using the reparameterization trick \cite{kingma2013auto} with the latent mean variable, the latent log variance variable, and the standard normal distribution, we obtain three latent variables $z_{1},z_{2},z_{3}$. After that, We use a single convolutional layer to obtain the predicted segmentation $\hat{y}$.

Finally, we leverage a fully convolutional network with 7 layers for image reconstruction. The input for the source domain includes the ground truth segmentation $y$ and the latent variable $z$, whereas the input for the target domain is the predicted segmentation $\hat{y}$.

\subsection{Structure Mutual Information Estimation}
In this subsection, we aim to estimate the mutual information between the segmentation outcome $\hat{y}$ and the reconstruction output $R$ in the source and target domains. The mutual information can be formulated as:

\begin{equation}
     \widehat{\mathcal{I}}\left(\hat{y} ; R\right)=D_{K L}\left(\mathbb{P}_{\hat{y} R} \| \mathbb{P}_{\hat{y}} \otimes \mathbb{P}_{R}\right)
\end{equation}
The KL divergence between joint distribution $\mathbb{P}_{\hat{y} R}$ and marginal distribution $\mathbb{P}_{\hat{y}}\otimes \mathbb{P}_{R}$ can be written as its dual representation\cite{donsker1975asymptotic} as below:
\begin{equation}
    D_{K L}(\mathbb{P}_{\hat{y} R} \| \mathbb{P}_{\hat{y}} \otimes \mathbb{P}_{R})=\sup _{T: \Omega \rightarrow \mathbb{R}}( \mathbb{E}_{\mathbb{P}_{\hat{y} R}}[T]-\log \left(\mathbb{E}_{\mathbb{P}_{\hat{y}} \otimes \mathbb{P}_{R}}\left[e^{T}\right]\right))
\end{equation}
where T is the set of all possible neural network.

Inspired by \cite{hjelm2018learning}, we are interested in automatically maximizing the mutual information rather than manually obtaining the exact value for mutual information. The mutual information maximization process can be formulated as:

\begin{equation}
    \widehat{\mathcal{I}}\left(\hat{y} ; R\right) = \mathbb{E}_{\mathbb{P}_{\hat{y} R}}\left[-\operatorname{sp}\left(-T\left(\hat{y}, R\right)\right)\right]-\mathbb{E}_{\mathbb{P}_{\hat{y}} \otimes \mathbb{P}_{R}}\left[\operatorname{sp}\left(T\left(\hat{y}, R^{\prime}\right)\right)\right]
\label{mi}
\end{equation}
where $R^{\prime}$ is an input sampled from $R$, and $\operatorname{sp}(z)=\log \left(1+e^{z}\right)$ is the softplus function.

The next step is to estimate the joint and marginal distribution of $\hat{y}$ and $R$ using contrastive learning. First, we design three estimators in the MI block \cite{hjelm2018learning}. The original paired $R$ and $\hat{y}$ serve as the anchor and the positive point, respectively. We then shuffle $R$ randomly to obtain the negative point. To fuse the data together, we upsample the 40$\times$40 feature map and downsample the 160$\times$160 feature map. Before entering the estimator block, the anchor and negative point will go through two convolutional layers, whereas the positive point will go through three convolutional layers.

For the Global MI Estimation block, we concatenate the positive points with anchor and negative points, pushing the anchor away from the negative points and pulling the anchor towards the positive point. For the Local MI Estimation block, we extract the high-level semantics using fully connected layers. Next, we concatenate the semantic information with the positive point to acquire the locality information, followed by two convolutional layers for contrastive learning.

Finally, motivated by \cite{hjelm2018learning, chen2020structure}, we adopt the prior matching \cite{makhzani2015adversarial} strategy to constrain the visual representations according to standard normal distribution. Specifically, in the prior information estimation block, the positive point will go through fully connected layers and output the prior information.

\begin{figure*}[t]
    \centering
    \includegraphics[width=12cm]{Model/MINE.pdf}
    \caption{The architecture of Structure Mutual Information Estimation (SMIE) block. We use the reconstruction image as anchor, the shuffle reconstructed images as negative points, and the segmented image as positive points. The Global \& Local Mutual Information (MI) Estimation Block follows contrastive learning schemes to maximize mutual information, whereas the prior information matching block align the positive point with the standard normal distribution. Finally, the sum of the outputs score from these three blocks serves as the loss function for $\mathcal{L}_{MI}$.}
    \label{MINE}
\end{figure*}

\begin{figure}[t]
    \centering
    \includegraphics[width=8cm]{Model/loss.pdf}
    \caption{The loss function of the proposed method. The blue line refers to the reconstruction loss. The red line indicates the segmentation loss between the predicted segmentation and ground truth segmentation. The orange line illustrates the domain discrepancy loss in the latent space. The green line refers to the structure mutual information loss.}
    \label{Loss}
\end{figure}

\subsection{Loss function}
For the segmentation part, we aim to maximize the joint log-likelihood $\log p_{\theta_{S}}(x, y)$ of the dataset.
\begin{theorem}
\begin{equation}
    \begin{aligned}
&\log p_{\theta_{S}}(x, y) \\
\geq &\left(\mathcal{\epsilon}+ \widehat{\mathcal{I}}_{q \phi_{S}}(x, y, z)-H_{q \phi_{S}}(z)+\log \frac{p_{\theta_{S}}(x, y)}{q_{\phi_{S}}(x, y)}\right)\\
&-D_{K L}\left(q_{\phi_{S}}(z \mid x) \| p_{\theta_{S}}(z)\right) \\
&+E_{q_{\phi_{S}}(z \mid x)} [\log p_{\theta_{S}}(x \mid y, z)] \\
&+E_{q_{\phi_{S}}(z \mid x)} [\log p_{\theta_{S}}(y \mid z)]
\end{aligned}
\label{loss_fun}
\end{equation}
where $\epsilon, H_{q \phi_{S}(z)}, \log \frac{p_{\theta_{S}}(x, y)}{q_{\phi_{S}}(x, y)}$ are all constant.
\end{theorem}
\begin{proof}
Detailed proof will be in the supplementary material.
\end{proof}
For the domain discrepancy loss, we minimize it explicitly as the latent space obeys normal distribution. \\
Therefore, our loss function (Fig. \ref{Loss}) contains structure mutual information estimation loss  $\mathcal{L}_{MI}$(Eq.\ref{loss_fun} line 1) reconstruction loss $\mathcal{L}_{recon}$(Eq.\ref{loss_fun} line 2,3), segmentation loss $\mathcal{L}_{seg}$(Eq.\ref{loss_fun} line 4), and domain discrepancy loss $\mathcal{L}_{D}$.

\subsubsection{Reconstruction Loss}
The reconstruction loss is same as the design in VAE. We use neural network $q_{\phi}(z|x)$ with parameter $\phi$ to approximate the posterior distribution $p_{\theta}(z | x)$ for latent variable $z$. In other words, we attempt to minimize the KL divergence of $q_{\phi}(z|x)$ and $p_{\theta}(z|x)$:
\begin{equation}
\begin{aligned}
    &D_{K L}\left(q_{\phi}(z|x) \| p_{\theta}(z|x)\right) \\
    &= D_{K L}\left(q_{\phi}(z|x) \| p_{\theta}(z)\right)-E_{z \sim q_{\phi}}[\log p_{\theta}(x|z)]
\end{aligned}
\label{vae}
\end{equation}
The first term aims to minimize the KL divergence between the neural network $q_{\phi}(z|x)$ and the prior distribution $p_{\theta}(z) \sim N(0,I)$, where $I$ is the identity matrix. The neural network $q_{\phi}(z|x)$ performs variational reasoning upon $u$ and $\sigma^{2}$ to approximate $0$ and $I$, respectively. With the reparameterization trick\cite{vae}(red arrows in Fig. \ref{Preliminary}), we can get:
\begin{equation}
    D_{K L}\left(q_{\phi}(z|x) \| p_{\theta}(z)\right)=\frac{1}{2} \left(\sigma^{2}+u^{2}-\log \sigma^{2}-1\right)
\end{equation}
The second term in equ[\ref{vae}] is to maximize the likelihood of $x$. This can be calculated by cross entropy loss between the input $x$ and the reconstruction output $R$:
\begin{equation}
    \mathcal{L}_{ce} = -(x \log (R)+(1-x) \log (1-R))
\end{equation}
Finally, we get the reconstruction loss:
\begin{equation}
    \mathcal{L}_{recon} = D_{KL} + \mathcal{L}_{ce}
\end{equation}

\subsubsection{Segmentation Loss}
The segmentation loss helps us minimize the loss between the predicted segmentation $\hat{y}$ and the ground truth segmentation $y$. We apply cross-entropy loss, which is formulated as below:
\begin{equation}
    \mathcal{L}_{seg} = -(y \log (\hat{y})+(1-y) \log (1-\hat{y}))
\end{equation}

\subsubsection{Domain Discrepancy Loss}

The Domain Discrepancy Loss helps reduce the domain discrepancy between the source and the target domain in the latent space. In the UDA-VAE framework, \cite{wu2021unsupervised} has proved that optimizing the distance explicitly would have better accuracy than adversarial training. As the latent space is regularized into a standard normal distribution, we can calculate the distance analytically. The Domain Discrepancy Loss is formulated as below:

\begin{equation}
\begin{aligned}
    & \mathcal{L}_{D} = D(q_{\phi_{S}}(z),q_{\phi_{T}}(z))\\
    &= \int [q_{\phi_{S}}(z)-q_{\phi_{T}}(z)]^{2}dz \\
    & = \frac{1}{M^{2}} \sum_{i=1}^{M} \sum_{j=1}^{M}\left[k\left(x_{S_{i}}, x_{S_{j}}\right)+k\left(x_{T_{i}}, x_{T_{j}}\right)-2 k\left(x_{S_{i}}, x_{T_{j}}\right)\right]
\end{aligned}
\end{equation}
where $M$ is the batch size. $i,j$ are $ith,jth$ element in one batch. As the variables in latent space obey standard normal distribution. The kernel function $k$ is:
\begin{equation}
    k\left(x_{S_{i}}, x_{T_{j}}\right) = (2\pi)^{-\frac{1}{2}} e^{-\frac{1}{2}[\frac{(u_{S_{i}}-u_{T_{j}})^{2}}{\sigma_{S_{i}}^{2}+\sigma_{T_{j}}^{2}}+\log(\sigma_{S_{i}}^{2}+\sigma_{T_{j}}^{2})]}
\end{equation}

\subsubsection{Structure Mutual Information Loss}
As discussed in equ[\ref{mi}], we design a contrastive learning framework to estimate the joint and marginal distribution of $\hat{y}$ and $R$.
To maximize $\widehat{\mathcal{I}}(\hat{y} ; R)$, we design a global MI estimation block, a local MI estimation block, and a prior information matching block.
\begin{equation}
    \mathcal{L}_{MI} = -(\alpha \widehat{\mathcal{I}}(\hat{y} ; R)_{Global}+ \beta \widehat{\mathcal{I}}(\hat{y} ; R)_{Local}+\gamma \widehat{\mathcal{I}}_{Prior})
\end{equation}
where $\alpha, \beta, \gamma$ are set as 0.5, 1.0, 0.1. $\widehat{\mathcal{I}}_{Prior} = log(\mathcal{N})+log(1-\hat{y})$, where $\mathcal{N}$ is the standard normal distribution.
\subsubsection{Total Loss}
The total loss is defined as:
\begin{equation}
\begin{aligned}
    \mathcal{L}_{total} &= (c1\mathcal{L}_{recon}+c2\mathcal{L}_{seg}+c3\mathcal{L}_{MI})_{source} \\
    &+ (c1\mathcal{L}_{recon}+
    c2\mathcal{L}_{seg}+c3\mathcal{L}_{MI})_{target} \\
    &+ c4\mathcal{L}_{D}
\end{aligned}
\end{equation}
where c1, c2, c3, c4 are empirically set as 1e-2, 1, 1e-1, 1e-5, respectively.

\section{Experiments}
\begin{table}[t]
\setlength{\tabcolsep}{1mm}{
\begin{tabular}{lllllllll}
\hline
\multicolumn{6}{c}{Model Components}                  & \multicolumn{3}{c}{Dice ($\%$)}                         \\ \hline
Base & SR & Att & Global & Local & Prior & MYO            & LV             & RV             \\ \hline
\checkmark      &    &             &        &       &       & 68.42          & 84.41          & 72.59          \\
\checkmark      & \checkmark  &             &        &       &       & 68.56          & 84.07          & 74.06          \\
\checkmark       & \checkmark  & \checkmark           &        &       &       & 68.30          & 84.91          & 74.72          \\
\checkmark       & \checkmark  & \checkmark           & \checkmark      &       &       & 69.25          & 84.70          & 75.63          \\
\checkmark       & \checkmark  & \checkmark           & \checkmark      & \checkmark     &       & 68.49          & 87.50          & \textbf{77.37} \\ \hline
\checkmark       & \checkmark  &             & \checkmark      & \checkmark     & \checkmark     & \textbf{70.75} & \textbf{88.64} & 75.82          \\
\checkmark       & \checkmark  & \checkmark           & \checkmark      & \checkmark     & \checkmark     & \textcolor{blue}{69.81}        & \textcolor{blue}{87.54}        & \textcolor{blue}{77.13}         \\
 \hline
\end{tabular}}
\caption{The Ablations of model components for MS-CMRSeg Dataset from \textbf{CT to MRI}. Base: UDA-VAE \cite{wu2021unsupervised}. SR: Sequential Reparameterization. Att: Attention. Global: Global MI Estimation Block. Local: Local MI Estimation Block. Prior: Prior Matching. The best score for UDA from CT to MRI is in \textbf{bold} while the second-best score is in \textcolor{blue}{blue}.}
\label{ablation}
\end{table}

\begin{table}[t]
\setlength{\tabcolsep}{1mm}{
\begin{tabular}{lllllll}
\hline
 & \multicolumn{3}{c}{Dice ($\%$)}                                                                                & \multicolumn{3}{c}{ASSD (mm)}                                                                                \\ \hline
                & MYO                                                   & LV                                                    & RV                                                    & MYO                                                   & LV                                                    & RV                                                    \\ \hline
NoAdapt
& 14.50                                                 & 34.51                                                 & 31.10                                                 & 21.6                                                     & 11.3                                                     & 14.5                                            \\

CFDNet \cite{wu2020cf}          & \begin{tabular}[c]{@{}l@{}}64.21\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}81.39\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}72.30\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}2.81\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.41\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}4.91\\ \end{tabular} \\
SIFA \cite{chen2020unsupervised}          & \begin{tabular}[c]{@{}l@{}}67.69\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}83.31\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{79.04}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}2.56\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.44\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{2.13}\\ \end{tabular} \\
UDA-VAE \cite{wu2021unsupervised}        & \begin{tabular}[c]{@{}l@{}}68.42\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}84.41\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}72.59\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}2.39\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}2.59\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.97\\ \end{tabular} \\ \hline
UDA-VAE++       & \begin{tabular}[c]{@{}l@{}}\textbf{70.75}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{88.64}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}75.82\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{2.02}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{2.27}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.62\\ \end{tabular} \\
\hline
\end{tabular}}
\caption{Unsupervised Domain Adaptation for MS-CMRSeg Dataset from \textbf{CT to MRI}. The best score for Dice$\uparrow$ and ASSD$\downarrow$ are in \textbf{bold}.}
\label{CT_MRI_Dice_IOU}
\end{table}

\begin{table}[t]
\setlength{\tabcolsep}{1mm}{
\begin{tabular}{lllllll}
\hline
         & \multicolumn{3}{c}{Dice ($\%$)}                                                                                & \multicolumn{3}{c}{ASSD (mm)}                                                                                \\ \hline
                         & MYO                                                   & LV                                                    & RV                                                    & MYO                                                   & LV                                                    & RV                                                    \\ \hline
NoAdapt & 12.32                                                 & 30.24                                                 & 37.25                                                 & 24.9                                                     &    10.4                                                  & 16.7                                           \\

CFDNet \cite{wu2020cf}                   & \begin{tabular}[c]{@{}l@{}}57.41\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}78.44\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}77.63\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.61\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.87\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}2.49\\ \end{tabular} \\
SIFA \cite{chen2020unsupervised}                  & \begin{tabular}[c]{@{}l@{}}60.89\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}79.32\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{82.39}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.44\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.65\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}1.80\\ \end{tabular} \\
UDA-VAE \cite{wu2021unsupervised}                 & \begin{tabular}[c]{@{}l@{}}58.58\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}79.43\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}80.43\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.53\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}3.27\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}2.04\\ \end{tabular} \\ \hline
UDA-VAE++                & \begin{tabular}[c]{@{}l@{}}\textbf{68.74}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{85.08}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}81.42\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{2.34}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{2.61}\\ \end{tabular} & \begin{tabular}[c]{@{}l@{}}\textbf{1.71}\\ \end{tabular} \\
\hline
\end{tabular}}
\caption{Unsupervised Domain Adaptation for MS-CMRSeg Dataset from \textbf{MRI to CT}. The best score for Dice $\uparrow$ and ASSD$\downarrow$ are in \textbf{bold}.}
\label{MRI_CT_Dice_IOU}
\end{table}

\begin{table*}[t]
\centering
\begin{tabular}{lllllllllll}
\hline
Methods      & \multicolumn{5}{c}{Dice ($\%$)}                                                      & \multicolumn{5}{c}{ASSD (mm)}                                                      \\ \hline
             & MYO           & LA            & LV            & RA            & RV            & MYO           & LA            & LV            & RA            & RV            \\ \hline
NoAdapt      & 0.08        & 3.08          & 0.00          & 0.74         & 23.9          & --            & --            & --            & --            & --            \\
PnP-AdaNet \cite{dou2019pnp}      & 32.7          & 49.7          & 48.4          & 62.4          & 44.2          & 6.89          & 22.6          & 9.56          & 20.7          & 20.0          \\
SIFA \cite{chen2020unsupervised}        & 37.1          & 65.7          & 61.2          & 51.9          & 18.5          & 11.8          & 5.47          & 16.0          & 14.7          & 21.6          \\
UDA-VAE \cite{wu2021unsupervised}     & 47.0          & 63.1          & 73.8          & 71.1          & 73.4          & 4.73          & 5.33          & 4.30          & 6.97          & 4.56          \\ \hline
UDA-VAE++    & \textbf{51.4} & \textbf{65.9} & \textbf{76.5}          & \textbf{73.0}          & \textbf{75.5} & \textbf{3.88} & \textbf{5.23} & \textbf{3.78}          & \textbf{6.25}          & \textbf{4.06} \\
\hline
\end{tabular}
\caption{Unsupervised Domain Adaptation for MM-WHS Dataset from \textbf{CT to MRI}. The best score for Dice$\uparrow$ and ASSD$\downarrow$ are in \textbf{bold}.}
\label{CT_MRI_Dice_ASST}
\end{table*}

Segmentation image visual details
\begin{figure*}
    \centering
    \includegraphics[width=2.65cm]{segmentation/segmentation1/LGE.png}
    \includegraphics[width=2.65cm]{segmentation/segmentation1/gt.png}
    \includegraphics[width=2.63cm]{segmentation/segmentation1/cfd.png}
    \includegraphics[width=2.64cm]{segmentation/segmentation1/varda.png}
    \includegraphics[width=2.63cm]{segmentation/segmentation1/woa.png}

    \\
    \includegraphics[width=2.65cm]{segmentation/segmentation2/LGE.png}
    \includegraphics[width=2.65cm]{segmentation/segmentation2/gt.png}
    \includegraphics[width=2.63cm]{segmentation/segmentation2/cfd.png}
    \includegraphics[width=2.66cm]{segmentation/segmentation2/varda.png}
    \includegraphics[width=2.64cm]{segmentation/segmentation2/woa.png}

    \caption{Segmentation output from MS-CMRSeg Dataset (CT to MRI). From left to right: MRI, Ground truth, CFDNet\cite{wu2020cf}, UDA-VAE\cite{wu2021unsupervised}, UDA-VAE++. For the segmentation, we use yellow, green, and dark green to represent RV, MYO, and LV, respectively.}
    \label{Visual_Seg}
\end{figure*}

\begin{figure*}
    \centering
    \includegraphics[width=2cm]{recon/img/0.png}
    \includegraphics[width=2cm]{recon/img/1.png}
    \includegraphics[width=2cm]{recon/img/2.png}
    \includegraphics[width=2cm]{recon/img/3.png}
    \includegraphics[width=2cm]{recon/img/4.png}
    \includegraphics[width=2cm]{recon/img/5.png}
    \includegraphics[width=2cm]{recon/img/6.png}
    \includegraphics[width=2cm]{recon/img/7.png} \\
    \includegraphics[width=2cm]{recon/mask/0.png}
    \includegraphics[width=2cm]{recon/mask/1.png}
    \includegraphics[width=2cm]{recon/mask/2.png}
    \includegraphics[width=2cm]{recon/mask/3.png}
    \includegraphics[width=2cm]{recon/mask/4.png}
    \includegraphics[width=2cm]{recon/mask/5.png}
    \includegraphics[width=2cm]{recon/mask/6.png}
    \includegraphics[width=2cm]{recon/mask/7.png} \\
    \includegraphics[width=2cm]{recon/vae-uda/0.png}
    \includegraphics[width=2cm]{recon/vae-uda/1.png}
    \includegraphics[width=2cm]{recon/vae-uda/2.png}
    \includegraphics[width=2cm]{recon/vae-uda/3.png}
    \includegraphics[width=2cm]{recon/vae-uda/4.png}
    \includegraphics[width=2cm]{recon/vae-uda/5.png}
    \includegraphics[width=2cm]{recon/vae-uda/6.png}
    \includegraphics[width=2cm]{recon/vae-uda/7.png} \\
    \includegraphics[width=2cm]{recon/woa/0.png}
    \includegraphics[width=2cm]{recon/woa/1.png}
    \includegraphics[width=2cm]{recon/woa/2.png}
    \includegraphics[width=2cm]{recon/woa/3.png}
    \includegraphics[width=2cm]{recon/woa/4.png}
    \includegraphics[width=2cm]{recon/woa/5.png}
    \includegraphics[width=2cm]{recon/woa/6.png}
    \includegraphics[width=2cm]{recon/woa/7.png} \\

    \caption{Reconstruction Images from MS-CMRSeg Dataset (CT to MRI). From top to bottom row: MRI images, corresponding segmentation ground truth, UDA-VAE, UDA-VAE++. }
    \label{Visual_Recon}
\end{figure*}

\subsection{Implementation Details}
We use Adam optimizer \cite{kingma2014adam} and Pytorch framework \cite{paszke2019pytorch} to train our model for 30 epochs. The learning rate is initialized at 1e-4 and is reduced by 10 \% after every epoch. The batch size is 12, which takes about 1 hour to converge on a single NVIDIA Tesla V100 GPU. The network weight follows Xavier initialization \cite{glorot2010understanding}. Neither gradient scaling nor gradient clipping is applied during training.

\subsection{Datasets}

We consider two benchmark datasets for model performance comparison, including Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge dataset \cite{zhuang2016multi} and Multi-Sequence Cardiac MR Segmentation (MS-CMRSeg) Challenge dataset \cite{zhuang2018multivariate}

\textbf{MM-WHS Dataset} contains 20 labeled CT images and 20 labeled LGE-MRI images, which are unpaired. Each image is cropped to a size of 240$\times$220.

\textbf{MS-CMRSeg Dataset} contains 35 labeled CT images and 45 labeled LGE-MRI images, which are also not paired. Each image is cropped to a size of 192$\times$192.

Similar to \cite{wu2020cf, wu2021unsupervised}, we include the following three structures in the given images for segmentation: the myocardial (MYO), the left ventriculus (LV), and the right ventriculus (RV). For both datasets, We remove the MRI ground truth during CT to MRI experiments and remove the CT ground truth during MRI to CT experiments. The train-test split strategy is consistent with \cite{dou2019pnp, chen2020unsupervised, wu2020cf, wu2021unsupervised}

\subsection{Evaluation Metrics}
We use three commonly used evaluation metrics for segmentation, including Dice coefficient (\%) and Average Symmetric Surface Distance (ASSD) (mm). The Dice coefficient calculates the agreement between the predicted segmentation and ground truth segmentation by dividing the intersection area by the total pixels in both images. ASSD measures the segmentation accuracy at boundary-level using the Euclidean
distance of the closest surface voxels between two segmentations \cite{heimann2009comparison}. All metrics are in the format of the mean. A higher Dice and a lower ASSD score indicate better segmentation performances.

\subsection{Ablation Study}
In this subsection, we investigate the contribution of our model components via an ablation study, using the Dice coefficient as the evaluation metric. Specifically, we gradually add individual components and see how the presence of that component will affect the model performances.

Table \ref{ablation} shows the quantitative results of the ablation study. It is shown that most proposed modules will improve the Dice scores. For example, sequential reparameterization, adding Attention, Global, and Local MI estimation increases the Dice score for MYO, LV, and RV. Besides, prior info matching will slightly decrease RV but significantly increase MYO and LV, indicating overall performance improvement.

\subsection{Qualitative Comparison}

Fig. \ref{Visual_Seg} shows the visual comparison for segmentation among different models, including CFDNet, UDA-VAE, and the proposed UDA-VAE++. It is shown that the proposed UDA-VAE++ leads to the best structure representation, the best edge preservation, and is the closest to the ground truth. In contrast, CFDNet and UDA-VAE have a significant segmentation error between MYO, RV, and the background.

Fig. \ref{Visual_Recon} displays the visual comparison for reconstruction between different models. Here we only compare UDA-VAE++ with UDA-VAE since UDA-VAE is the only related work that considers image reconstruction. It is shown that the proposed UDA-VAE++ displays significantly better reconstruction than UDA-VAE. UDA-VAE++ has excellent edge preservation, shape representation, and class segmentation. In comparison, UDA-VAE has a significant amount of blurs and artifacts.

\subsection{Quantitative Comparison}

The quantitative comparison utilize several state-of-the-art models, including PnP-AdaNet \cite{dou2019pnp}, SIFA \cite{chen2020unsupervised}, UDA-VAE \cite{wu2021unsupervised}, and the proposed UDA-VAE++.

Table \ref{CT_MRI_Dice_IOU} shows the quantitative comparison for UDA with MS-CMRSeg Dataset (CT to MRI). We can find that the proposed UDA-VAE++  has the best Dice and ASSD score in terms of MYO and LV segmentation. While SIFA has a slight advantage for RV segmentation, it underperforms our model for all other metrics in the table. Therefore, we can conclude that the proposed UDA-VAE++ has the best performance in this experiment.

Table \ref{MRI_CT_Dice_IOU} shows the quantitative comparison for UDA with MM-WHS Dataset (CT to MRI). We can observe that the proposed UDA-VAE++ has the best Dice and ASSD score in terms of MYO and LV segmentation. Despite SIFA's success in Dice score at RV segmentation, it significantly underperforms our method for all other metrics. Overall, the proposed UDA-VAE++ has the best result in this comparison.

Table \ref{CT_MRI_Dice_ASST} shows the quantitative comparison for UDA with MS-CMRSeg Dataset (MRI to CT). We can see that the proposed UDA-VAE++ has the best Dice and ASSD score in terms of all segmentations (MYO, LA, LV, RA, RV).

\section{Conclusion}
This paper introduces UDA-VAE++, an unsupervised domain adaptation framework for cardiac segmentation. Through mutual information estimation and maximization, we make the reconstruction and segmentation task mutually beneficial. Moreover, we introduce the sequential reparameterization design, allowing information flow between multi-scale latent space features. Extensive experiments demonstrate that our model achieved state-of-the-art performances on benchmark datasets. Our future work will integrate the proposed mutual information estimation block with self-supervised domain adaptation methods. We also aim to extend our framework to other medical image segmentation tasks (e.g., brain image segmentation).

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redpajama_set_name: RedPajamaArXiv


text:
It's relatively easy to follow, but you do have to pay attention (it doesn't cater to you and repeat things).
I've enjoyed that. Carving dude's detective work in ep 3 pays off in ep 4. Didn't need to follow it closely, but a nice through-line if you did.
Has Omar showed up yet?
Just. His first appearance was a big deal, his second... just to let us know he's coming back. Love how he's better at this than the cops..
Lindley said: ↑
I sometimes found it hard to keep all the various characters straight. It's not that bad in the first season though.
I don't have any of the names, really. And a few of the characters who only show up from time to time, I'm not really clear on exactly who they are. The story always makes the relationships clear, but exactly which person is which isn't obvious beyond the main cast.
I'm still not totally clear on how the judge has so much authority over everything...?
[Ultimately, it's pretty much what I expected it to be, which is why I delayed so long in watching it. It's a police procedural.
Another piece of what makes it work, and why it relies so heavily on plot, is that it feels like a documentary. It is convincing as a portrayal of the "real" police. Which was part of why the Pryzb. little plot was disappointing - could a real cop be such a screw-up? But then, the show actually gets some lee-way in its story-telling, because it's so plausible the rest of the time. I can imagine the writers talking about what they had to tone down, so people would believe it. Does anyone know of any interviews or commentary where the realism is discussed?
Yassim, Jul 2, 2010
Which was part of why the Pryzb. little plot was disappointing - could a real cop be such a screw-up?
He was married to the right person. That's got to count for something, no? The first few episodes there don't exactly paint a very flattering portrait of the Baltimore police. They're low on everything, it seems; cash, manpower, talent, you name it. They keep typewriters around and there aren't any typewriters married to Valchek's daughter. (Although...)
I'd consider the the show moving characters around the board where they need to be the main strike against realism.
Spoiler: seasons two, three, first part of four
McNulty is sent to the boat, and hey presto, we get a season about dockside crime. Since it's connected to Prez and Prez loved the stuff that happened in season one it's also a good device to get the whole team back into the narrative.
McNulty also meets and dates Carcetti's campaign manager in season three because hey, why not? When season four rolls around we have two ex-cops and one ex-con having changed their careers over to assisting young people, which is the major theme of that season. Also Herc has a stint of mayoral police duty. And so on. If the plot decided to wander into Baltimore's fine dining establishments we'd probably have Lester Freamon retiring to become a food critic and Greggs investigating across the way.
Kegg, Jul 2, 2010
Politics. You don't want the judge upset with you.
Lindley, Jul 2, 2010
That, and everyone is either brilliant or a "hump". Why isn't anyone just mediocre at their job?
But they do make the cops look like fools.
Other than go to the media, it's not clear to me how or why a judge has any authority over these characters. Until the judge mentioned his brother's charity that might help out the force, I didn't (and still don't really) see why everyone's afraid the judge isn't happy. In what sense do they answer to him?
Isn't Herc pretty much mediocre? Also Santangelo and some of the other homicide detectives (Norris, Holley? It's been a long time, I need to watch the series again).
That reminds me of one realistic thing I loved about The Wire. In a lot of police detective shows, Kojack and so on, the higher ranking sergeants and lieutenants (usually the star) are out on the street working cases. But Sgt. Landsman just sits on his ass, eating and looking at porn, and making life miserable for everyone else. A true bureaucratic supervisor!
They don't. But he's got political connections, travels in the same circles as the mayor and the council and people they do answer to.
J.T.B., Jul 3, 2010
stj Rear Admiral Rear Admiral
They need to keep the judge sweet because he can screw them over whenever they want a warrant or make it difficult to get prosecutions. And, to be blunt, because the judge is part of their line of defense against prosecutions for their own crimes. Despite what Fox News and Fox TV shows tell you, planting evidence, suppressing evidence of innocence, intimidating witnesses, brutality and torture, etc. are all crimes. Also, taking bribes, robbing dealers, sleeping with prostitutes, gambling, taking drugs, etc., are all crimes. The presumption that the police are not testilying could be undermined with just a little fairness from the judge. They don't want to get on his bad side.
stj, Jul 3, 2010
Hunter X Rear Admiral Rear Admiral
Another piece of what makes it work, and why it relies so heavily on plot, is that it feels like a documentary. It is convincing as a portrayal of the "real" police. Which was part of why the Pryzb. little plot was disappointing - could a real cop be such a screw-up?
Actually, my guess is that a lot of the really out there anecdotes and character quirks are based on real life situations the showrunners heard about or encountered in their previous careers as a police officer and a journalist in Baltimore. I'm not talking in terms of where the overall plot goes, just throwaway background things like Prez shooting up his car.
Hunter X, Jul 3, 2010
Hunter X said: ↑
Actually, my guess is that a lot of the really out there anecdotes and character quirks are based on real life situations the showrunners heard about or encountered in their previous careers as a police officer and a journalist in Baltimore.
A lot of the little details sound like that; in particular the idea that the guys playing cards cut Snot into their game every time - though he kept stealing from them - 'cause this is America. It's precisely the kind of bizarre little line that sounds plain too weird to have been made up.
But it struck me also the show's relationship to character is like a journalist's article: An article might begin with some details of an individual to give the topic they're discussing a human face, and then jump off into the wider context of that issue. I think that's pretty much how The Wire uses its characters; they're there to illuminate the issue and add some colour and drama to it, but none of them are really ends in themselves.
Ziggy and his duck are based on a real person (and bird) from Baltimore, fyi
Alidar Jarok, Jul 3, 2010
mimic Rear Admiral Rear Admiral
You're completely right about that particular scene. I read Simon's book Homicide and the Snot Boogie thing is lifted straight from his experience shadowing the police for a year.
mimic, Jul 3, 2010
Ultimately, it's pretty much what I expected it to be, which is why I delayed so long in watching it. It's a police procedural. I hate police procedurals. While it may be an arced police procedural with an understanding of the messy realities of politics is still a police procedural and that stuff's liable to put me to bed - not that the series has ever quite caught me napping.
While on the surface it appears to be a police procedural, ultimately The Wire was the story of "the American city", and its purpose was to explore the workings of various organisations in the city and how they interact with one another. Unfortunately, HBO would never have picked up the show if that's the way David Simon and Ed Burns had pitched it to them. Since they had a history in writing about crime and law enforcement, they pitched their idea as a cross between The Sopranos (which was popular at the time) and a standard police procedural (which are always popular for some reason). The first season is a cop story, but their intention was for the story to explore how the police department and the Barksdale criminal organisation interact with one another, and to show the contrast in how they deal with internal matters.
The problem was that people thought that The Wire was a cop show, and most of the cast were working in the police department, so as the show expanded its focus into unions, politics, education and the media, they were always forced to have an investigation of some kind each season. The season that moved away from this format most was season 4, the police stories that season were a side-plot to the education and political storylines.
The Wire is as much an intellectual exercise as it is a plot or character based drama. That's why season 3 is my favourite; they took an interesting "what if" scenario and tried to explore it as best they could from a social and political perspective, and at the same time you have...
that brilliant character drama going on between Stringer and Avon.
Personally, I think that The Shield was way more of a police procedural show, especially in the first couple of seasons, but even as the arcs took over it maintained some elements from its early episodic nature. It's a great show in many ways, and it's almost worth watching all seven seasons just for the final episode, but having watched The Wire before watching The Shield I couldn't help but feel that the investigations in The Shield were far too simplistic: Someone commits a murder, Michael Chiklis beats someone up in order to find the murderer, rinse and repeat. Although, ultimately The Shield is probably the better character drama.
TheGodBen, Jul 4, 2010
firehawk12 Fleet Admiral Admiral
EXILE + ATTON = GUUUUUUSH!!!! (pic by aimo)
^ I basically agree with your Shield vs Wire comparison. The Wire is more... narrative focused, which is why I think as a "series" it is more interesting. But yeah, Mackey stands out as probably one of the best characters in television and his character ending is one of the most brilliant endings I've ever seen.
It's interesting to see what Simon and Ryan have done since though and how their styles haven't changed too much. Generation Kill and Treme are more about situations and weaving a structured and complete narrative while The Unit and Lie to Me are heavily character focused, with the episodes serving only as an excuse to mess up the characters in some way.
Heck, The Unit even took a page out of The Shield's first episode by offing a main character... although they saved it for a few seasons to make it more impactful.
firehawk12, Jul 4, 2010
That's not really how I saw it.
Spoiler: the wire
I saw it as more watching where the characters were going and gaining insight in to wherever they landed. I didn't read about the show as it happened so everything seemed to happen organically to me. The schools, docks, papers, political offices, ect. existed before and after the show focused on them. We see hints of them in seasons before they were the main focus. (Well, maybe not the docks in season 1.) There definitely was some coincidences in regards to the relationships but I didn't necessarily mind that. It was interesting how different characters interacted with each other.
As far as the Pryz character, there are plenty of fuckups that end up as cops. Particularly if you're a fuckup with connections. Heck I knew a guy that had no connections that was one mental breakdown away from becoming a cop. It was actually scary how many chances they gave him to totally mess up at the academy. Thankfully he did eventually have that breakdown, but it was an extraordinary circumstance that caused it. Had things just gone a little more right, this head-case douchebag would have ended up as a cop rather then selling counterfeit purses in china-town.
On the Shield vs. Wire front, I thought the Wire was significantly better. I'm saying this as someone who really liked the Shield. I thought it kind of strayed too far in to the... I don't know... cheesy action route? I accepted the show as unrealistic because it was entertaining, but sometimes it seemed like it went a little too far. Also the yearly gang war that Vick had to eventually turn the sides against each other or whatever got a little boring. I appreciated where it took it's characters, but I think they did it at the expense of the world they inhabited.
Aside from maybe Brother Mouzon, almost everything on the Wire was something I could accept as being real. Rather than putting the screws to all the characters, sometimes the situations resolved in a way where all the characters could do was shake their head and say "it is what it is" and move on, as often happens in real life. And they did move on, and we got to see what became of them when they did. Sometimes the good ones got fucked over, and the ones that didn't deserve it got the golden ticket. Sometimes people didn't get revenge, but life just went on.
The wire may not be my favorite show of all time, but I'm far more inclined to say it was the best show of all time than any other show I've watched.
Capt. Vulcan, Jul 4, 2010
Capt. Vulcan said: ↑
I saw it as more watching where the characters were going and gaining insight in to wherever they landed.
Spoiler: first part of season 4
But where they landed is very convenient for the plot. Prez becomes a teacher in a year the schools and the students are going to tie into other plots, and Herc has a stint as a cop defending the mayor right as the election starts. The characters are moved about where-ever the plot needs them to be, is my point.
Aside from maybe Brother Mouzon, almost everything on the Wire was something I could accept as being real.
Really. I bought Brother Mouzone, but that's probably more due to my rank ignorance of America than anything else. I mean, he's just an articulate hitman with a bit of a Nation of Islam/Malcolm X vibe.
It doesn't matter if he's realistic or not, he's still one of the most entertaining characters in the show. Seriously, I'd watch a Brother Mouzone spinoff.
While on the surface it appears to be a police procedural, ultimately The Wire was the story of "the American city", and its purpose was to explore the workings of various organisations in the city and how they interact with one another.
Maybe that happens in season five. I've almost concluded season four, and I'd still say this is a police procedural; just one that branches out a lot - season 4 far more then any of its previous years, to be fair... and perhaps not coincidentally this may also be my favourite year of the series to date.
Sure, there's more politics and other issues, but the bulk of the regulars remain policemen and a concern for the cop world remains consistent. It's more than a cop show but it's still a cop show, if that makes sense. That might be because of HBO restraints or whatever, but we're not talking about what The Wire could have been - we're talking about what it is.
But then, it's always more than a cop show but still a cop show - way back in season 1 where Yassim is watching he might note that our POV isn't limited to the cops, as some time has been spent with the drug dealers and even an addict. Of course, that sort of fits with the 'Sopranos' log-line - having Avon Barksdale and Stringer Bell do business at a strip club definitely has shades of David Chase's mafia saga (though I'm sure that also happens in, uh, real life.)
Unfortunately, HBO would never have picked up the show if that's the way David Simon and Ed Burns had pitched it to them.
The show had other problems too I read about. Apparently the political angle was originally going to be a spinoff series titled The Hall, but they had to fold that back into The Wire due to low ratings... which actually I think works. Given how densely arced this show is it might be confusing trying to find out how to alternate it and a spinoff when watching the show.
The Wire is as much an intellectual exercise as it is a plot or character based drama.
I'd say more. The Wire's heart is in its concepts, not its characters. The journalist analogy I used; they're here to provide the drama and put a human face on it, but the idea itself is what matters.
As an excerise, season three's idea of hell, what if they tolerate the drug trade in certain areas is interesting - but inevitable in its conclusions. The only real surprises for me were that Colvin actually went through with it, and that Royce actually hesitated about shutting it down.
The Wire got silly as it went on (some of the foolishness is revealed in spoilers above,) till it went completely ape even in the "character driven" cop story last season. But the much maligned procedural aspect of the story gave it a kind of backbone that could support willing suspension of disbelief.
The Shield on the other hand was based on the notion that brutal, corrup cops are the most effective cops. This is extraordinarily stupid on the face of it. To me, it stank of agenda: Cops have to break fiddling laws about civil rights to do their jobs. I could never force myself to watch an episode. Well, some people reject the concept of reality testing entertainment. The Shield didn't sound interesting because it was selling the pleasure in seeing a vicarious hero act out a political agenda (and then sufferes at the end, as a way of denying the implications of the pleasure in his actions.) AT least, that was the overwhelming impression I got. I suspect that the people who liked The Shield believe that cops have to break civil rights laws to do a good job, instead of thinking cops who break civil rights laws are by definition bad cops.
The Wire's characters mostly were not vicarious heroes, but like regular people. They weren't extraordinarily larger than life, routinely getting away with crap most people couldn't dream of pulling. The Wire's characters, being ordinary people, were part of a society, well, at least the city of Baltimore. They weren't satisfying fantasy figures who were masters of their destiny. Instead they were part of a system, which Burns and Simon insisted was a dysfunctional but immutable system (yes, this is logically nonsensical, but Burns and Simon had their own agenda, just a more sophisticated one than the usual ruck of screenwriters.) Being part of a system dimished them in some people's eyes, I think.
The romance or ideology of individualism often expresses itself as a perverse desire to see characters detached from any social or cultural background. Like figures in a painting detached from their background, such characters are less realistic. This is a kind of primitivism (just as that kind of art is often called primitivism,) or a symbolic convention, like monumental art where the Gods can be recognized because they're twice as big as the humans.
PS I may be misremembering my dates, but CSI (the real one, not the spinoffs,) killed off a major character in its first episode. Because CSI is the despised procedural, it doesn't count?
stj said: ↑
I could never force myself to watch an episode.
I haven't seen The Shield yet either. Thanks for confirming that you don't know anything about it so I don't try to use your opinion when deciding whether or not I should.
Instead they were part of a system, which Burns and Simon insisted was a dysfunctional but immutable system (yes, this is logically nonsensical, but Burns and Simon had their own agenda, just a more sophisticated one than the usual ruck of screenwriters.) Being part of a system dimished them in some people's eyes, I think.
Something that show pretty much directly addresses with McNulty. There's our individualistic go-getter cop who's hated by his boss and wants to get the bad guys that matter.
Probably. I've seen only one episode of a CSI show, Miami, and that was on an airplane and I wanted to see if it'd really begin with a lame David Caruso wisecrack in the teaser (it did).
It wasn't only The Brother, Omar rapidly achieved that status. Is it true that the show was rewritten after they saw his early performances to add more Omar?
Star Wolf, Jul 4, 2010
The plot didn't need Herc working at the mayor's office, he only worked there for a few episodes and the whole point of that plotline was that he saw mayor Royce banging his assistant. That storyline didn't impact the election plot at all, it was just there to explain how incompetent cops like Herc could be promoted to a position of authority.
I feel that the police-procedural element is the sugar-coated shell, but that the delicious chocolate centre is the show's exploration of the city of Baltimore. The police element is always there because that's how the show started out, but at the end of the day that's not what the show is about.
That's not what The Shield was like at all, although I must admit that I did think it was like that after watching the pilot. In reality, The Shield detailed how police officers that ignore the protections the law provides ultimately became criminals themselves because they felt their extreme methods deserved extra reward, so they got involved in the drug trade and various other criminal activities. Once they realised how far they had fallen they tried to escape and be good cops again, but they were trapped in a death spiral that was going to take them all down.
Also, there was only 4 main members of the Strike Team, the rest of the cops were normal and worked within the law, some of them even dedicated their careers in an attempt to bring Mackey and the rest of the team down. My favourite character was Dutch, the nerdy and law-abiding homicide detective that used his understanding of psychology to get suspects to confess. The worst thing he ever did involved a cat, but I wont spoil it.
They weren't extraordinarily larger than life, routinely getting away with crap most people couldn't dream of pulling.
Actually, The Shield was based on the real-world Rampart Police division and their anti-gang CRASH unit, many of whom got up to the same sorts of things that the Strike Team did in the show.
That said, The Wire is by far the more realistic show, The Shield exaggerated things for dramatic impact all the time. I think The Wire was clearly the better show, but I can definitely understand why some people prefer The Shield.
Admittedly it's not as laugh out loud funny as "Mafioso sees a shrink," but no one needs me to decide whether they think cops need to be brutal to do their jobs. Either you take propositions like that seriously, or you don't. They could just remember the Ramparts scandal. The Closer may not get any credit around this bbs but at least they have a clue about the real world, even if they're wary about using too much of it.
In other words, I don't need to see a show to decide the premise is foolish. It's up to fans of The Shield who've seen the show to explain either, I'm misinformed about the premise, or, how cops really do need to be brutal and ignore civil rights to do their jobs.

redpajama_set_name: RedPajamaCommonCrawl


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David Vito Gregoli – Music Producer
* "Working with Vito was a life-changing experience. He was patient and helped me with my learning curve which I really appreciated. The album exceeded my expectations!"
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* "Vito is remarkably talented, creative, and professional. His deep knowledge of of all types of music always amazes me! Hhe allows a collaborative process that leads to the best possible results."
"Being a music producer these days can require you wear a lot of hats" say Vito.
"Your the arranger, the engineer, the computer technician, the A&R guy, most of the players, the best friend who will be totally honest with you, and sometimes the mixer and mastering engineer. Not an easy job".
"I'm fortunate that I've worked in many different genres and many different situations. I think that gives me a unique perspective. Growing up in a family whose business was TV/Film gave me a good sense of the arc of a musical piece. And that's important because whether your singing lyrics or playing an instrumental piece, it's about a story."
Indeed, Vito's musical journey has been nothing if not diverse. His earliest musical explorations were rooted in Pop, Rock, Jazz and Blues, and shades of The Beatles, Eric Clapton and Jeff Beck can be spotted in his work to this day. By the time he was in his teens, he was already hailed as a guitar virtuoso and working regularly in the L.A. club scene. "I was lucky I could grow a beard, so I passed for legal age," quips Vito. Surrounded by older and more experienced musicians, Vito soaked up technique and style, but took a right turn in college towards Classical Music. "I've loved history since I was a kid, and Classical Music was the soundtrack to every era. At university, my emphasis was in Renaissance music, composition, and later, Spanish guitar." This Latin influence would later flower into "Organic Nylon," a Classical guitar CD laced with jazzy, South-of-the-Border flavor and sensuality.
Vito's classical studies naturally led him to scoring. "I grew up in a theatrical family, and my sister was always calling on me to create music for her shows," says Vito. His keen dramatic instincts have been tapped for music in film, TV and theatre projects.
Vito uses Mark of the Unicorn's "Digital Performer" DAW exclusively.
Endorsements include Blue Microphones, Godin and Simon & Patrick guitars, Fishman Pickups, and Shubb Capos.
Read the testimonials from Vito's clients and checkout the albums produced by Vito below>>>.
Please use the contact page for any professional inquires.
Prem Vidu – Encounter
"Encounter - Medicine Songs from Ma". Songs - from my heart to yours.
The lyrics are from the heart and soul of a man that has found his true path through the music and spirituality."
Keith Hannaleck – Muzikman Reviews
Co produced and engineered by David Vito Gregoli
Kimberly Haynes feat. David Vito Gregoli – Holiday EP
This EP features 3 new holiday songs from acclaimed singer/songwriter Kimberly Haynes, + bonus track.
Award winning singer/songwriter Kimberly Haynes interpets 3 holiday classics plus one bonus track.
Included is a new arrangements of the classic "O Holy Night" featuring Maksim Velichkin on cello, a holiday favorite "A Christmas Song" and the previously released "Have Yourself A Merry Little Christmas". To round off the EP as a bonus track, Kimberly sings the old gospel song "In The Garden".
Co produced and performed w David Vito Gregoli
Kimberly Haynes - Narrow Bridge
Conscious new folk/classic rock music with singer/songwriter Mary Gospe
Mary Gospe is a singer/songwriter based in the San Francisco Bay Area whose style can be described as "New Folk/Classic Rock." Her songs are filled with empowering messages and a strong spiritual undercurrent.
"Co-created with producer, multi-instrumentalist, and well-known guitarist, David Vito Gregoli, "Time to Soar" encourages us to trust, leave fear and worry behind, and move forward with our "authentic self" into laughter, joy, love, and freedom. This is the kind of music that seeps into your soul and stays there.
Mary Gospe – Unity
Written for the 2017 Women's March in honor of women, men and children across the globe who join together in peace to catalyze positive social change
"The first single off of my upcoming album "Time To Soar." I wrote the song the morning of the January 2017 Women's March in honor of women, men and children across the globe who join together in peace to catalyze positive social change: women's equality, justice, equal pay, empowerment and more. "Replacing fear and hatred with compassion for the other."
"Working together, we are strong!" "Leading together, we'll bring change."
Produced by David Vito Gregoli
Mary Gospe - Time To Soar
Merrill Collins - Every Man, Woman and Child - Yoga Flow Suite
A beautifully realized collaboration by Collins & Gregoli, powerfully expressing the joyful spirit of the universe.
Three tracks created for yoga practitioners on the "Every Man Woman and Child" theme by Merrill Collins, created as a way to set intention and dedication to the principals of the Universal Declaration of Human Rights. A beautifully realized, unhurried sonic collaboration by Merrill Collins and David Vito Gregoli, powerfully expressing the joyful spirit of the universe. Featuring Kimberly Haynes on vocals, Maksim Velichkin on cello, Laura Halladay on flutes.
Kimberly Haynes - In The Garden
Dedicated to mothers and the great mother
Bathe in the enchanting soaring vocals of Kimberly Haynes in her exquisite new single release, "In The Garden". This hymn is my adaptation of a gospel song written in 1912 by American songwriter C. Austin Miles, a former pharmacist who served as editor and manager at Hall-Mack publishers for 37 years. According to Miles' great-granddaughter, the song was written "in a cold, dreary and leaky basement in Pitman, New Jersey that didn't even have a window in it let alone a view of a garden."
The garden really is within….. I adapted the song to reflect an honoring our Great Mother Earth, our human mothers, and our spiritual mothers, all of whom give so much to us all, The Great Mother Earth leading by example. Here is my tribute to The Mother.
Mary Gospe - Winter Solstice
Experience the holidays via the Winter solstice with singer/songwriter Mary Gospe
Mary Gospe is a singer/songwriter based in the San Francisco Bay Area whose style can be described as "Celtic, Retro, Americana." Her songs are filled with empowering messages and a strong spiritual undercurrent. Mary is currently working on her debut album with multi-instrumentalist/producer David Vito Gregoli
Kimberly Haynes – You're the One
Reinvisioned remixes from award winning album "Awaken Me"
The EP features a new radio edit of "You're The One That I Want/Govinda Jaya", a brand new dance/chill version of the album title song "Awaken Me (Global Mix)" and "Do I Dare (Earth Mix)" from the award winning Awaken Me album.
Featuring performances by Byron Metcalf, Aeb Byrne, Christo Pellani, Jamie Papish, Butto, Garreth Laffely, Cooper Madison Lanier, and co-producer David Vito Gregoli.
Kimberly Haynes - Pinta
Germain Vasquez' praise of mother earth as interpreted by acclaimed singer/songwriter Kimberly Haynes
Germain Vasquez' praise of mother earth beautifully interpreted by acclaimed singer/songwriter Kimberly Haynes. Accompanied by David Vito Gregoli
Kimberly Haynes – Awaken Me
Thoughtful and Inspiring, diverse and illuminating, a blissful journey for the soul.
Singer-songwriter Kimberly Haynes draws from various musical styles to create a beautiful mosaic of authentic and joyful stories.
"Kimberly paints musical portraits from a diverse palette of influences…Her expressive voice ranges from soft to soaring with thoughtful and inspiring lyrics that touch the heart and illuminate the spiritual quest."
– Michael Diamond, Music and Media Focus
Featuring performances by Grammy® winner Peter Kater, Tina Malia, Byron Metcalf, Jesus Florido, Ken Stacey and producer David Vito Gregoli.
Produced by Kimberly Haynes and David Vito Gregoli
"Lyons' intimate yet ethereal vocals, and her sincere lyrics combined with poignant soaring music & melodies create a lush space of serenity.
"The Light Within charted #3 on the Billboard New Age Charts, and "Eternal Now (ft. Ron Korb)" was nominated for an Independent Music Award and a Hollywood Music in Media Award. Some beautiful reviews have been coming in – you can read what people are saying, listen to the songs, and read the liner notes here: The Light Within, If you'd like to purchase a CD, the only place to do so is julietlyons.bandcamp.com. Digital downloads and streams are available everywhere (Spotify, Apple Music, iTunes, Pandora, Amazon, etc). Thank you for the incredible support as I've sent this album into the world!"
The song "Flow" was produced and arranged by David Vito Gregoli
Classic christmas song from award winning singer/songwriter Kimberly Haynes
A beautiful and tender version of one of the most beloved christmas songs, "Have Yourself A Merry Little Christmas". Dedicated to her children and husband Brian. Her melifuous and soothing voice will bring a smile and he warmth of the season to all who hear.
Accompanied by co producer David Vito Gregoli
All Good Dreamers
"All of the songs that I chose for All Good Dreamers comment on the complexities of love..."
…dreams and just being a human in today's world. Navigating your way through all the noise and tragedy that seem to roll through our lives daily. This music helps me through it."
"A perfect balance of current cuts and nostalgic gems, All GoodDreamers is the kind of spent a Friday night at home with" – the Baub Show
Produced by and all instruments performed by David Vito Gregoli
Lokah Samastah
Lokah Samastah: May There Be Peace.
The meaning of this chant is very close to her heart and a message the world needs right now, "May all beings everywhere be happy and free, and may the thoughts, words, and actions of my own life contribute in some way to that happiness and to that freedom for all."
nicholchaseyoga.com/
For years, actor/singer Daphne Ashbrook has been asked, "When are you going to finally record an album?"
Inspired by singer/songwriters like Joni Mitchell, Shawn Colvin & Patty Griffin, the artist chose tunes that profoundly touched her heart. "I love songs that tell stories, like 'Useless Desires' by Patty Griffin & Joni's 'Furry Sings the Blues'. They make you feel something, something real." Just as her acting skills range from the dramatic to the comedic, Daphne also was attracted to songs with ironic humor, like Griffin's "One Big Love", the Talking Heads' "This Must Be the Place" & the jazzy Billie Holiday-esque "I Don't Need No Doctor," which is a tongue-in-cheek nod to her best known film role in Doctor Who. "Of course, Rickie Lee Jones' 'Horses' is my ode to my daughter," Daphne says.
Produced and arranged by multi instrumentalist David Vito Gregoli
Winter, Snow and Strings
Sophisticated and unique classical/new age holiday music
"You will recognize the melodies, but not right away. They will come to you like a sunrise, like some distant, longed for memory or like a dream coming back to you. For some, the music may simply be a brief, relaxing respite. But I found them too evocative to be anything but heart pinching. The arrangements are masterful: understated and eloquent; sophisticated in their simplicity, but more gut wrenching, soulful, and heartfelt (without trying) than any music I've heard this side of Mozart."
Produced and arranged by David Vito Gregoli
Tibetan chants to the deity Tara in it's Green and White emanations.
Jamyang Sakya is Sakya Dagmo (Holy Mother) of the Sakya Khon family — one of Tibet's religious family lineages.
Arya Tara is the Saviouress who responds to the needs and sufferings of beings.
"This is not music, this is prayer. Her voice on the recording is full of clarity and innocence, and this is passed along to the one listening to this chanting…This is of the highest quality and sincerity of heart that you can possibly find."
Songs of Kuan Yin
Heart Opening Music to Invoke the Divine Feminine
In the legend of Kuan Yin, the ascended bodhisattva turns away from the gates of Nirvana in order to stay in the world until all sentient beings are free from suffering.
With Songs of Kuan Yin, a gathering of some of today's most passionate and talented female performers bring you music inspired by this beloved deity—10 devotional offerings to immerse you in the "cool, cleansing waters of Kuan Yin's compassion."
…including "Prayer for Kuan Yin" by Donna De Lory and David Vito Gregoli
Prana: Healing Tones for Life
East/west ambient fusion of Indian vocals on a luscious bed of Soundscape
Virtuoso Indian singer Mala Ganguly teams with multi instrumentalist and producer David Vito Gregoli.
"Instead of flooding us with loud bhajans, the music gently invites the listener in, to find the magic of the yoga in the sounds.
The vocals gently teach us how to use our ears to do yoga simply by listening intently...It can help us through our own struggles to manage stress, and experience music in a new way."
New World Jazz Order
Alternatively sexy and spiritual, playful and dramatic, its a tour de force of world jazz
DHARMA BUDS are David Vito Gregoli (guitarist/multi-instrumentalist) and David Joyce (keyboardist/vocalist). "New World Jazz Order" goes beyond 'smooth jazz' – (this is not Kenny G), using World Music textures and edgy crossover grooves to paint a compelling soundscape.
Tim Jordan Kirtan – Heart and Spirit
David Vito Gregoli - All You Need Is Love
Mashup of the Beatles "All You Need Is Love" coupled with Yes' "Your Move".
David Vito Gregoli is a Composer/Producer/Multi Instrumentalist. His music is diverse & intense as well as passionate and romantic. He is owner and primary producer for Dharmapala Records. This is one of his rare vocal performances. He is joined by singer/actor Daphne Ashbrook.
This song is dedicated to the memory of John Lennon who have been 79 this year.


Triton Rugby Club extends many thanks to Dr Greg & Christina Moreno, owners of MEDHERO, Advanced Urgent Care and Telemedicine provider in San Clemente, for their kind donation of a fully stocked triage medical kit to our program. Your kind support is appreciated.
Read the SC Times story here.


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The Royal We by Heather Cocks & Jessica Morgan
Title: The Royal We
Author: Heather Cocks & Jessica Morgan
"I might be Cinderella today, but I dread who they'll think I am tomorrow. I guess it depends on what I do next."
American Rebecca Porter was never one for fairy tales. Her twin sister, Lacey, has always been the romantic who fantasized about glamour and royalty, fame and fortune. Yet it's Bex who seeks adventure at Oxford and finds herself living down the hall from Prince Nicholas, Great Britain's future king. And when Bex can't resist falling for Nick, the person behind the prince, it propels her into a world she did not expect to inhabit, under a spotlight she is not prepared to face.
Dating Nick immerses Bex in ritzy society, dazzling ski trips, and dinners at Kensington Palace with him and his charming, troublesome brother, Freddie. But the relationship also comes with unimaginable baggage: hysterical tabloids, Nick's sparkling and far more suitable ex-girlfriends, and a royal family whose private life is much thornier an more tragic than anyone on the outside knows. The pressures are almost too much to bear, as Bex struggles to reconcile the man she loves with the monarch he's fated to become.
Which is how she gets into trouble.
Now, on the even of the wedding of the century, Bex is faced with whether everything she's sacrificed for love—her career, her home, her family, maybe even herself—will have been for nothing.
I'm not going to lie, I basically didn't know about The Royal We until I was offered a chance to review it. I might have seen it here and there, but it was never a book that I paid attention to. But once I read the description, it sounded like it could be a lot of fun. What I didn't expect, was how much I absolutely loved this book.
The last thing Rebecca Porter expected when she decided to study abroad at Oxford was to fall in love. In going to Britain, she was looking for adventure, for new experiences. Not romance. The one thing she was definitely not looking for was living down the hall from Britain's next king, the prince she just so happens to fall in love with. But after spending time with Nick, Bex can't help but fall for him. But being with a prince also means being thrown into a world Bex never thought she would be a part of. Dating Nick isn't simple. As much as she just wants to spend time with Nick, Bex also has to share him with the rest of the world. A world that doesn't exactly think she's what's best for the future king of Britain. Together, Bex and Nick find a way to make it work, but on the day before they are set to get married, things are about to fall apart as Bex starts questioning everything she's had to sacrifice to be with the person she loves.
I'm kind of a sucker for anything involving royalty of any kind. I blame it on the fact that I spent most of my middle and high school years reading Meg Cabot's Princess Diaries series (to this day, it's still one of my favourite series of all time). But despite my love of all things royal in books, I hadn't really heard about Heather Cocks and Jessica Morgan's The Royal We. When I did finally hear about it, I thought it had the potential to be a book that would be right up my alley. So I gave it shot. I started reading. and the next thing I new I just couldn't stop. I knew I was very likely to enjoy the story, I just didn't expect to love it as much as I did and to get so caught up in it. I was having trouble resisting the urge to just read it while I was sitting at my desk at work, trying not to count down the minutes until I would be able to read it again. This was just such a fun story. I loved the way it was set up, how it was spread out over the course of seven years and I got to see Nick and Bex's entire relationship, the ups and the downs and everything in between. It made me that much more invested in their story, made me want to root for them that much more. But the other great thing about the story was just how fun it was. So many times while I was reading, I would find myself laughing at the situation the characters were in, at something Bex would say, or at Freddie's antics. I just had a good time while reading The Royal We.
Be was seriously the best. I loved reading this story from her perspective. She just came across as very genuine. When the story starts, she's just a girl looking for adventure while studying abroad. And then she met a prince and things got infinitely more interesting. But the thing that I loved about Bex was that she wasn't in love with Prince Nicholas, she was just in love with Nick, the guy who lived down the hall from her and who she watched horrible supernatural soaps with. Granted as the story moved forward she had to accept that being in love with Nick also meant being in love with Prince Nicholas. And Nick, well he was a prince, so there's that. But really, I loved Nick when he was just being Nick as opposed to forced to be Prince Nicholas. And he and Bex were just great together. I loved seeing them together and seeing their relationship develop, even though it wasn't without it's ups and downs. But as much as I loved Bex and Nick, the character who stole the show was Nick's brother Freddie. Any time Freddie was in the story, a smile just broke out on my face, I couldn't help it. He was easily my favourite character in the story, and honestly I want him to have his own book. I would love to see everything that goes on behind the fun and goofy facade he puts on for the world. Any time I got a glimpse of what was behind that facade, I loved him even more. So Freddie needs his own book, simple as that.
Heather Cocks and Jessica Morgan's The Royal We was unexpected. It's the book that I didn't see coming and took me completely by surprise. I enjoyed every single page of this story and if you're looking for what has got to be one of the most adorable love stories I've read recently, than give The Royal We a try. Odds are, you'll have the best time reading it.
Labels: ADULT, GRAND CENTRAL PUBLISHING, HEATHER COCKS, JESSICA MORGAN, REVIEW, THE ROYAL WE
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Blog Tour - The Queen of Bright and Shiny Things b...


This Budget motel in Buena, New Jersey is a 15-minute drive to downtown Vineland. This pet-friendly motel offers business services, free on-site parking and guest rooms with free Wi-Fi.Budget Lodge features a 27-inch TV with cable in every room.
The spacious rooms offer modern appliances including a microwave and refrigerator.Luciano Conference Center is a 20-minute drive from the Buena Budget Lodge. Clayton Stadium is also 20 minutes away and downtown Vineland is within 6 miles of the motel.
This property is also rated for the best value in Buena! Guests are getting more for their money when compared to other properties in this city.
Casino Pier is an amusement park situated on a pier, in Seaside Heights, New Jersey, United States. Casino Pier was partially destroyed, and indefinitely closed, on October 29, 2012, after part of the pier collapsed into the Atlantic Ocean due to the storm surge generated by Hurricane Sandy.Despite this, park management successfully rebuilt the pier's lower deck, and re-opened with limited rides in July 2013.Before the storm, the pier extended approximately 300 feet (100 meters) into the Atlantic Ocean, from the narrow strip of the Barnegat Peninsula (approx. 6 blocks) in which Seaside Heights is situated.
Liberty Science Center is an interactive science museum and learning center located in Liberty State Park in Jersey City in Hudson County, New Jersey, United States.The center, which first opened in 1993 as New Jersey's first major state science museum, has science exhibits, the largest IMAX Dome theater in the United States, numerous educational resources, and the original Hoberman sphere, a silver, computer-driven engineering artwork designed by Chuck Hoberman.
The New Jersey State Museum is located at 205 West State Street in Trenton, New Jersey, overlooking the Delaware River. The Museum is operated as part of the New Jersey Department of State. The Museum's main collection of specimens, artifacts and objects date back to items collected in the early 19th century. The Museum also includes a 140-seat planetarium and a 384-seat auditorium.The New Jersey State Museum serves a broad region between New York and Philadelphia. Because the admission fee is 'suggested', and programs are free or offered at a very low-cost, the Museum is accessible to visitors with low- to moderate incomes.
Space Farms Zoo and Museum is a combination of a natural environment for animals and a historical museum located at 218 Route 519, in the Beemerville section of Wantage Township in Sussex County, New Jersey, in the United States.Founded in 1927, the zoo is home to a wide selection of mammals and reptiles of the North America region and all over the world, including some endangered species. At one time, Space Farms was host to the largest bear in captivity in the world, a Kodiak bear named Goliath. The 2,000-pound (900 kg) Goliath still stands tall in front of a diverse taxidermy exhibit in the main hall of the museum. He was preserved in an imposing upright position after his death in 1991.His abnormally large skull is upstairs, where one can compare it to skulls of other animals.
All children are welcome. Free! Up to two children under 12 years stay free of charge when using existing beds. Any additional older children or adults are charged USD 10 per person per night when using existing beds. Any additional older children or adults are charged USD 10 per night for extra beds. The maximum number of extra beds/cribs in a room is 1. Any type of extra bed is upon request and needs to be confirmed by management. Additional fees are not calculated automatically in the total cost and will have to be paid for separately during your stay.
It was a quick get away for me and my boyfriend to rekindle our relationship. The room was nice for the price.we would definitely come back!!
For the price this place hit the mark! Staff was very friendly and helpful.The managers and staff were most helpful, pleasant and accommodating. Thank you for accepting my dog. She wondered what we were doing there and she thanks you for allowing her to stay close to her guardians.
Room was very clean and cozy. Staff was very pleasant and accomodating.We stayed overnight so we could go to my High School Reunion. We had stayed 5 years ago for the same reason and found it a pleasant place to stay. Prices are reasonable, rooms are clean, and place is close to everything you need for comfort.
The hotel was very nice. The room was very comfortable and the staff was friendly.This motel has been very nice each time we have stayed.We will come again.
I liked the excellent price, and that the room overall was appeared recently updated and was clean. Bed was comfortable.The room was clean and up to date. Linens looked fresh, and the furniture new.
I love it very convient and staff very nice very clean I stay here all the time.Great room for the price! Last min booking for new years!It was great, nice and quiet.


Sick of snow - PMC Time!
Why why why do I ride the Pan-Mass Challenge?
It's been a few tough weeks or what month. Ridiculous, work is stupid. Layler is a good doggie. Friday night was an hour and 30 minutes of dog solitary (in the mudroom) while we grabbed a bite. She was stupid good, didn't tear up a thing, yay. We'll work up to it. Now that spring is here, we'll see how it goes.


This week, we are updating some of the pictures from our customers. We produce the best after-market ABS fairing and we also offer the best performance parts to our customers in collaboration with our partners to produce the best high quality parts. As used and trusted by many racing teams.


In one of the better championship matches it took all three games and a 25-21 win to secure the championship!!!!! Skocats did not lose a match this season and this was only the second time it took three games. It was definitely a team effort and everyone on deck. Great season capped off by a championship!!!!!
I have the song stuck in my head! With an energetic start sand sharks never let up and never looked back. A true team effort, when asked who the player of the game everyone agreed the entire team performance was the reason for the championship win!!! Good job!!!


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