U-Bench: A Comprehensive Understanding of U-Net through 100-Variant
  Benchmarking
Over the past decade, U-Net has been the dominant architecture in medical
image segmentation, leading to the development of thousands of U-shaped
variants. Despite its widespread adoption, there is still no comprehensive
benchmark to systematically evaluate their performance and utility, largely
because of insufficient statistical validation and limited consideration of
efficiency and generalization across diverse datasets. To bridge this gap, we
present U-Bench, the first large-scale, statistically rigorous benchmark that
evaluates 100 U-Net variants across 28 datasets and 10 imaging modalities. Our
contributions are threefold: (1) Comprehensive Evaluation: U-Bench evaluates
models along three key dimensions: statistical robustness, zero-shot
generalization, and computational efficiency. We introduce a novel metric,
U-Score, which jointly captures the performance-efficiency trade-off, offering
a deployment-oriented perspective on model progress. (2) Systematic Analysis
and Model Selection Guidance: We summarize key findings from the large-scale
evaluation and systematically analyze the impact of dataset characteristics and
architectural paradigms on model performance. Based on these insights, we
propose a model advisor agent to guide researchers in selecting the most
suitable models for specific datasets and tasks. (3) Public Availability: We
provide all code, models, protocols, and weights, enabling the community to
reproduce our results and extend the benchmark with future methods. In summary,
U-Bench not only exposes gaps in previous evaluations but also establishes a
foundation for fair, reproducible, and practically relevant benchmarking in the
next decade of U-Net-based segmentation models. The project can be accessed at:
https://fenghetan9.github.io/ubench. Code is available at:
https://github.com/FengheTan9/U-Bench.
68e71b637ae125f9582e6985
Fenghe Tang
68e71b637ae125f9582e6986
Chengqi Dong
68e71b637ae125f9582e6987
Wenxin Ma
68e71b637ae125f9582e6988
Zikang Xu
68e71b637ae125f9582e6989
Heqin Zhu
68e71b637ae125f9582e698a
Zihang Jiang
68e71b637ae125f9582e698b
Rongsheng Wang
68e71b637ae125f9582e698c
Yuhao Wang
68e71b637ae125f9582e698d
Chenxu Wu
68e71b637ae125f9582e698e
Shaohua Kevin Zhou
U-Bench: A Comprehensive Understanding of U-Net through 100-Variant
  Benchmarking
Over the past decade, U-Net has been the dominant architecture in medical
image segmentation, leading to the development of thousands of U-shaped
variants. Despite its widespread adoption, there is still no comprehensive
benchmark to systematically evaluate their performance and utility, largely
because of insufficient statistical validation and limited consideration of
efficiency and generalization across diverse datasets. To bridge this gap, we
present U-Bench, the first large-scale, statistically rigorous benchmark that
evaluates 100 U-Net variants across 28 datasets and 10 imaging modalities. Our
contributions are threefold: (1) Comprehensive Evaluation: U-Bench evaluates
models along three key dimensions: statistical robustness, zero-shot
generalization, and computational efficiency. We introduce a novel metric,
U-Score, which jointly captures the performance-efficiency trade-off, offering
a deployment-oriented perspective on model progress. (2) Systematic Analysis
and Model Selection Guidance: We summarize key findings from the large-scale
evaluation and systematically analyze the impact of dataset characteristics and
architectural paradigms on model performance. Based on these insights, we
propose a model advisor agent to guide researchers in selecting the most
suitable models for specific datasets and tasks. (3) Public Availability: We
provide all code, models, protocols, and weights, enabling the community to
reproduce our results and extend the benchmark with future methods. In summary,
U-Bench not only exposes gaps in previous evaluations but also establishes a
foundation for fair, reproducible, and practically relevant benchmarking in the
next decade of U-Net-based segmentation models. The project can be accessed at:
https://fenghetan9.github.io/ubench. Code is available at:
https://github.com/FengheTan9/U-Bench.
U-Bench is a comprehensive benchmark evaluating 100 U-Net variants across 28 datasets and 10 imaging modalities, focusing on statistical robustness, zero-shot generalization, and computational efficiency.
U-Net U-Bench U-Score zero-shot generalization computational efficiency statistical robustness model advisor agent