4
$\begingroup$

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 (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)?

$\endgroup$
  • $\begingroup$ 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 to all the papers that reference it so try looking through it, you should definitely find something relevant. $\endgroup$ – Brale Oct 26 '19 at 18:35
  • 1
    $\begingroup$ Here's a very related question (if not a duplicate): What are some good alternatives to U-Net for biomedical image segmentation?. $\endgroup$ – nbro Jun 13 at 0:06
3
$\begingroup$

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, 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, popular for Brain and Lesion images, Multi-Scale Attention for MRIs, etc. A recent paper that describes an interesting 3D FCNN architecture is HyperDense-Net, widely used for multi-modal tasks in medical image segmentation.

| improve this answer | |
$\endgroup$
1
$\begingroup$

You can find leaderboards as well as code at this address.

For now, HRNetV2 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

| improve this answer | |
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.