Answers.Body: <p>U-Net and U-Net inspired architectures have been quite popular in the medical image-related tasks ever since it was first introduced. There have been several improved versions of U-Net designed for specific tasks that followed. One such example is <a href="https://arxiv.org/abs/1804.03999v3" rel="nofollow noreferrer">Attention U-Net</a>, extremely popular for Pancreas Segmentation.</p>
  
  <p>Other examples of architectures that have achieved state-of-the-art results in image segmentation tasks in recent years include <a href="https://arxiv.org/abs/1603.05959v3" rel="nofollow noreferrer">Multi-Scale 3DCNN + CRF</a>, popular for Brain and Lesion images, <a href="https://arxiv.org/abs/1906.02849" rel="nofollow noreferrer">Multi-Scale Attention</a> for MRIs, etc. A recent paper that describes an interesting 3D FCNN architecture is <a href="https://arxiv.org/abs/1804.02967v2" rel="nofollow noreferrer">HyperDense-Net</a>, widely used for multi-modal tasks in medical image segmentation.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-28T15:49:50.953Z
Answers.LastActivityDate: 2019-10-28T15:49:50.953Z
Answers.UserDisplayName: Tanmay Agrawal
Answers.Body: <p>You can find leaderboards as well as code at <a href="https://paperswithcode.com/task/semantic-segmentation" rel="nofollow noreferrer">this address</a>.</p>
  
  <p>For now, <a href="https://arxiv.org/pdf/1908.07919.pdf" rel="nofollow noreferrer">HRNetV2</a> leads the game.</p>
  
  <p>The U-Net architecture is part of a broad family of network architectures that aggregate multi-scale features to extract finer details useful for semantic segmentation. Examples are Feature Pyramidal Networks (FPN), Hourglass, Encoder-Decoder, MatrixNet, etc...</p>
  
  <p><a href="https://i.sstatic.net/oKEhJ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/oKEhJ.png" alt="enter image description here"></a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-10-30T10:12:42.020Z
Answers.LastActivityDate: 2019-10-30T10:12:42.020Z
Answers.UserDisplayName: Louis Lac
Body: <p>I recently started looking for networks that focus on image segmentation tasks related to biomedical applications. I could not miss the publication <a href="https://arxiv.org/abs/1505.04597" rel="nofollow noreferrer">U-Net: Convolutional Networks for Biomedical Image Segmentation</a> (2015) by Ronneberger, Fischer, and Brox. However, as deep learning is a fast-growing field and the article was published more than 4 years ago, I was wondering if anyone knows other algorithms that yield better results for image segmentation tasks?  And if so, do they also use a U-shape architecture (i.e. contraction path then expansion path with up-conv)? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-10-26T18:35:22.077Z
Comments.Text: You should try doing some research on your own through Google Scholar. According to it the paper that you linked has been referenced 8722 times. Here is a [link](https://scholar.google.hr/scholar?cites=10845403114495995712&as_sdt=2005&sciodt=0,5&hl=hr) to all the papers that reference it so try looking through it, you should definitely find something relevant.
Comments.UserDisplayName: Brale
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-13T00:06:53.393Z
Comments.Text: Here's a very related question (if not a duplicate): [What are some good alternatives to U-Net for biomedical image segmentation?](https://ai.stackexchange.com/q/19943/2444).
Comments.UserDisplayName: nbro
Tags: <machine-learning><convolutional-neural-networks><reference-request><image-segmentation><u-net>
Title: What are the best algorithms for image segmentation tasks?
UserDisplayName: Denis Joly
ThreadText: # What are the best algorithms for image segmentation tasks?
  
  (Asked by: username_0 on 2019-10-25)
  
  I recently started looking for networks that focus on image segmentation tasks related to biomedical applications. I could not miss the publication [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) (2015) by Ronneberger, Fischer, and Brox. However, as deep learning is a fast-growing field and the article was published more than 4 years ago, I was wondering if anyone knows other algorithms that yield better results for image segmentation tasks? And if so, do they also use a U-shape architecture (i.e. contraction path then expansion path with up-conv)?
  
  **Tags:** machine-learning, convolutional-neural-networks, reference-request, image-segmentation, u-net
  > **Comments:**
  > **username_1**: You should try doing some research on your own through Google Scholar. According to it the paper that you linked has been referenced 8722 times. Here is a [link](https://scholar.google.hr/scholar?cites=10845403114495995712&as_sdt=2005&sciodt=0,5&hl=hr) to all the papers that reference it so try looking through it, you should definitely find something relevant.  
  > **username_2**: Here's a very related question (if not a duplicate): [What are some good alternatives to U-Net for biomedical image segmentation?](https://ai.stackexchange.com/q/19943/2444).  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_3 on 2019-10-28. Score: 3
  
  U-Net and U-Net inspired architectures have been quite popular in the medical image-related tasks ever since it was first introduced. There have been several improved versions of U-Net designed for specific tasks that followed. One such example is [Attention U-Net](https://arxiv.org/abs/1804.03999v3), extremely popular for Pancreas Segmentation.
  
  Other examples of architectures that have achieved state-of-the-art results in image segmentation tasks in recent years include [Multi-Scale 3DCNN + CRF](https://arxiv.org/abs/1603.05959v3), popular for Brain and Lesion images, [Multi-Scale Attention](https://arxiv.org/abs/1906.02849) for MRIs, etc. A recent paper that describes an interesting 3D FCNN architecture is [HyperDense-Net](https://arxiv.org/abs/1804.02967v2), widely used for multi-modal tasks in medical image segmentation.
  
  
  ---
  
  ### Answer by username_4 on 2019-10-30. Score: 1
  
  You can find leaderboards as well as code at [this address](https://paperswithcode.com/task/semantic-segmentation).
  
  For now, [HRNetV2](https://arxiv.org/pdf/1908.07919.pdf) leads the game.
  
  The U-Net architecture is part of a broad family of network architectures that aggregate multi-scale features to extract finer details useful for semantic segmentation. Examples are Feature Pyramidal Networks (FPN), Hourglass, Encoder-Decoder, MatrixNet, etc...
  
  [![enter image description here](https://i.sstatic.net/oKEhJ.png)](https://i.sstatic.net/oKEhJ.png)
  
  
  ---
  
  