U-Net and its variants for Medical Image Segmentation : A short review
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Abstract (arXiv)
The paper is a short review of medical image segmentation using U-Net and its variants. As we understand going through a medical images is not an easy job for any clinician either radiologist or pathologist. Analysing medical images is the only way to perform non-invasive diagnosis. Segmenting out the regions of interest has significant importance in medical images and is key for diagnosis. This paper also gives a bird eye view of how medical image segmentation has evolved. Also discusses challenge's and success of the deep neural architectures. Following how different hybrid architectures have built upon strong techniques from visual recognition tasks. In the end we will see current challenges and future directions for medical image segmentation(MIS).
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 7 references that are in the map. First posted on arXiv 17 Apr 2022.
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6 of 7 references.
References · 6
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,525
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,435
- 2018Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentationcited by 180
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