Medical Image Segmentation Review: The success of U-Net

• Published in: arXiv.org 2022-11
• Main Authors: Azad, Reza, Aghdam, Ehsan Khodapanah, Rauland, Amelie, Jia, Yiwei, Atlas Haddadi Avval, Bozorgpour, Afshin, Karimijafarbigloo, Sanaz, Cohen, Joseph Paul, Adeli, Ehsan, Merhof, Dorit
• Format: Article
• Language: English
• Subjects: Design optimization; Image segmentation; Medical imaging; Modular design; Task complexity; Taxonomy
• ISSN: 2331-8422

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://github.com/NITR098/Awesome-U-Net repository.