Answers.Body: <p>Hey i am working on my Bachelor thesis at the moment and use UNET in combination with a GAN for image segmentation. I spend the last 5 months on that, so on my tests, the new approach of januar 2020, called <a href="https://arxiv.org/pdf/1902.04049" rel="nofollow noreferrer">Multires-UNET</a> is quite a good choice for more texture orientated segmentation. I use the current <a href="https://github.com/nibtehaz/MultiResUNet/blob/master/MultiResUNet.py" rel="nofollow noreferrer">github</a> implementation. Its quite nice, maybe you notice that you can easyly tweak the number of parameters with "alpha" in the implementation, to scale the multiple Resnets in the Unet structure. </p>
  
  <p>I tried also other segmentation networks like Mask_RCNN with different backbones or tried to construct various types of CAE on my own, but always had to come back to a UNET like structure. Same goes for ResU-NET and R2U, the multires one worked better for my purposes cause i didnt need any kind of LSTM modules.</p>
  
  <p>Some examples which may clarify the difference in performance on a specific task:</p>
  
  <p>Original Image:
  <a href="https://i.sstatic.net/Wk9Ht.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Wk9Ht.png" alt="enter image description here"></a></p>
  
  <p>Ground-Truth:
  <a href="https://i.sstatic.net/rFjI4.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/rFjI4.png" alt="enter image description here"></a></p>
  
  <p>Classical UNET++ (Unet with skip-connections)(2.5 million parameters) more parameters (wide) didnt change the result.
  <a href="https://i.sstatic.net/SrCQP.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/SrCQP.png" alt="enter image description here"></a></p>
  
  <p>Multires-UNET (alpha=1.67, think was about 7 million parameters)
  <a href="https://i.sstatic.net/bvuiA.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/bvuiA.png" alt="enter image description here"></a></p>
  
  <p>Can you show some of ur microscopy images, how complex is ur task and what do you want to segment ?</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-04T13:08:26.943Z
Answers.Text: Hi there, may I ask, what does "more textured orientated segmentation" mean? How do you decide that a picture you are performing segmentation on is more textured orientated? :)
  Will certainly check it out. I can't show the images right now, after this coronavirus-situation I will get my hands on those
Answers.UserDisplayName: Nuwanda
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-04T13:29:11.830Z
Answers.Text: Hope the images give you some insigth, i trained it on about 500 handmade GT (used ImageJ for preprocessing, very useful tbh). Works way better for colored images, but Grayscale is a hard test for texture identification.
Answers.UserDisplayName: Paul Higazi
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-04T13:40:20.203Z
Answers.Text: With texture orientated i basicly meant that it searches for deeper or more color/rotation/graysclae - INVARIANT features. So features which you cant fool that easy with some augmentation for example like color shift.
Answers.UserDisplayName: Paul Higazi
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-12-15T23:40:30.300Z
Answers.Text: @PaulHigazi Is your first layer 572 by 572 if yes how large of your input image? How about evaluation matrix of your output against ground truth? Did you use Intersection of Union?
Answers.UserDisplayName: Cloud Cho
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-03T12:14:51.117Z
Answers.LastActivityDate: 2020-04-04T13:27:12.017Z
Answers.LastEditDate: 2020-04-04T13:27:12.017Z
Answers.UserDisplayName: Paul Higazi
Body: <p>Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best). </p>
  
  <p>Is there a neural network, that can compete with U-Net, in this case? </p>
  
  <p>I've spent the last few hours searching through scientific articles that are dealing with this topic, but haven't found a clear answer and I would like to know what other possibilities are. The best answers I found are that I could consider using <a href="https://arxiv.org/abs/1904.00592" rel="nofollow noreferrer">ResU-Net</a> (R2U-Net), <a href="https://arxiv.org/abs/1511.00561" rel="nofollow noreferrer">SegNet</a>, <a href="https://arxiv.org/abs/1812.00548" rel="nofollow noreferrer">X-Net</a> and backing techniques (<a href="https://arxiv.org/ftp/arxiv/papers/1906/1906.10400.pdf" rel="nofollow noreferrer">article</a>).</p>
  
  <p>Any ideas (with evidence, not necessarily)?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-11-10T13:03:27.400Z
Comments.Text: Check out this [paper](https://github.com/mahmoodlab/NucleiSegmentation).
Comments.UserDisplayName: Aray Karjauv
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-12-15T23:51:26.133Z
Comments.Text: If you want to detect anomaly on image, it would be a less layer neural network structure like the VGG16 (ref: https://towardsdatascience.com/anomaly-detection-in-images-777534980aeb)
Comments.UserDisplayName: Cloud Cho
Tags: <neural-networks><convolutional-neural-networks><reference-request><image-segmentation><u-net>
Title: What are some good alternatives to U-Net for biomedical image segmentation?
UserDisplayName: Nuwanda
ThreadText: # What are some good alternatives to U-Net for biomedical image segmentation?
  
  (Asked by: username_0 on 2020-04-02)
  
  Soon I will be working on biomedical image segmentation (microscopy images). There will be a small amount of data (a few dozens at best).
  
  Is there a neural network, that can compete with U-Net, in this case?
  
  I've spent the last few hours searching through scientific articles that are dealing with this topic, but haven't found a clear answer and I would like to know what other possibilities are. The best answers I found are that I could consider using [ResU-Net](https://arxiv.org/abs/1904.00592) (R2U-Net), [SegNet](https://arxiv.org/abs/1511.00561), [X-Net](https://arxiv.org/abs/1812.00548) and backing techniques ([article](https://arxiv.org/ftp/arxiv/papers/1906/1906.10400.pdf)).
  
  Any ideas (with evidence, not necessarily)?
  
  **Tags:** neural-networks, convolutional-neural-networks, reference-request, image-segmentation, u-net
  > **Comments:**
  > **username_1**: Check out this [paper](https://github.com/mahmoodlab/NucleiSegmentation).  
  > **username_2**: If you want to detect anomaly on image, it would be a less layer neural network structure like the VGG16 (ref: https://towardsdatascience.com/anomaly-detection-in-images-777534980aeb)  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2020-04-03. Score: 2
  
  Hey i am working on my Bachelor thesis at the moment and use UNET in combination with a GAN for image segmentation. I spend the last 5 months on that, so on my tests, the new approach of januar 2020, called [Multires-UNET](https://arxiv.org/pdf/1902.04049) is quite a good choice for more texture orientated segmentation. I use the current [github](https://github.com/nibtehaz/MultiResUNet/blob/master/MultiResUNet.py) implementation. Its quite nice, maybe you notice that you can easyly tweak the number of parameters with "alpha" in the implementation, to scale the multiple Resnets in the Unet structure.
  
  I tried also other segmentation networks like Mask\_RCNN with different backbones or tried to construct various types of CAE on my own, but always had to come back to a UNET like structure. Same goes for ResU-NET and R2U, the multires one worked better for my purposes cause i didnt need any kind of LSTM modules.
  
  Some examples which may clarify the difference in performance on a specific task:
  
  Original Image:
  [![enter image description here](https://i.sstatic.net/Wk9Ht.png)](https://i.sstatic.net/Wk9Ht.png)
  
  Ground-Truth:
  [![enter image description here](https://i.sstatic.net/rFjI4.png)](https://i.sstatic.net/rFjI4.png)
  
  Classical UNET++ (Unet with skip-connections)(2.5 million parameters) more parameters (wide) didnt change the result.
  [![enter image description here](https://i.sstatic.net/SrCQP.png)](https://i.sstatic.net/SrCQP.png)
  
  Multires-UNET (alpha=1.67, think was about 7 million parameters)
  [![enter image description here](https://i.sstatic.net/bvuiA.png)](https://i.sstatic.net/bvuiA.png)
  
  Can you show some of ur microscopy images, how complex is ur task and what do you want to segment ?
  
  > **Comments:**
  > **username_0**: Hi there, may I ask, what does "more textured orientated segmentation" mean? How do you decide that a picture you are performing segmentation on is more textured orientated? :) Will certainly check it out. I can't show the images right now, after this coronavirus-situation I will get my hands on those  
  > **username_3**: Hope the images give you some insigth, i trained it on about 500 handmade GT (used ImageJ for preprocessing, very useful tbh). Works way better for colored images, but Grayscale is a hard test for texture identification.  
  > **username_3**: With texture orientated i basicly meant that it searches for deeper or more color/rotation/graysclae - INVARIANT features. So features which you cant fool that easy with some augmentation for example like color shift.  
  > **username_2**: @PaulHigazi Is your first layer 572 by 572 if yes how large of your input image? How about evaluation matrix of your output against ground truth? Did you use Intersection of Union?  
  
  ---
  
  