图像分割
人工智能
计算机科学
计算机视觉
分割
尺度空间分割
图像处理
图像纹理
模式识别(心理学)
图像(数学)
医学影像学
作者
Reza Azad,Ehsan Khodapanah Aghdam,Amelie Rauland,Yulu Jia,Atlas Haddadi Avval,Afshin Bozorgpour,Sanaz Karimijafarbigloo,Joseph Cohen,Ehsan Adeli,Dorit Merhof
标识
DOI:10.1109/tpami.2024.3435571
摘要
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 has received tremendous attention from academic and industrial researchers who have extended it to address the scale and complexity created by medical tasks. These extensions are commonly related to enhancing the U-Net's backbone, bottleneck, or skip connections, or including representation learning, or combining it with a Transformer architecture, or even addressing probabilistic prediction of the segmentation map. Having a compendium of different previously proposed U-Net variants makes it easier for machine learning researchers to identify relevant research questions and understand the challenges of the biological tasks that challenge the model. In this work, we discuss the practical aspects of the U-Net model and organize each variant model into a taxonomy. 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. Furthermore, we provide a comprehensive implementation library with trained models. 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 a GitHub repository https://github.com/NITR098/Awesome-U-Net.
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