No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916813349060608 |
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| author | Jeon, Young Seok Yang, Hongfei Fu, Huazhu Feng, Mengling |
| author_facet | Jeon, Young Seok Yang, Hongfei Fu, Huazhu Feng, Mengling |
| contents | 3D models surpass 2D models in CT/MRI segmentation by effectively capturing inter-slice relationships. However, the added depth dimension substantially increases memory consumption. While patch-based training alleviates memory constraints, it significantly slows down the inference speed due to the sliding window (SW) approach. We propose No-More-Sliding-Window (NMSW), a novel end-to-end trainable framework that enhances the efficiency of generic 3D segmentation backbone during an inference step by eliminating the need for SW. NMSW employs a differentiable Top-k module to selectively sample only the most relevant patches, thereby minimizing redundant computations. When patch-level predictions are insufficient, the framework intelligently leverages coarse global predictions to refine results. Evaluated across 3 tasks using 3 segmentation backbones, NMSW achieves competitive accuracy compared to SW inference while significantly reducing computational complexity by 91% (88.0 to 8.00 TMACs). Moreover, it delivers a 9.1x faster inference on the H100 GPU (99.0 to 8.3 sec) and a 11.1x faster inference on the Xeon Gold CPU (2110 to 189 sec). NMSW is model-agnostic, further boosting efficiency when integrated with any existing efficient segmentation backbones. The code is avaialble: https://github.com/Youngseok0001/open_nmsw. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10814 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling Jeon, Young Seok Yang, Hongfei Fu, Huazhu Feng, Mengling Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 3D models surpass 2D models in CT/MRI segmentation by effectively capturing inter-slice relationships. However, the added depth dimension substantially increases memory consumption. While patch-based training alleviates memory constraints, it significantly slows down the inference speed due to the sliding window (SW) approach. We propose No-More-Sliding-Window (NMSW), a novel end-to-end trainable framework that enhances the efficiency of generic 3D segmentation backbone during an inference step by eliminating the need for SW. NMSW employs a differentiable Top-k module to selectively sample only the most relevant patches, thereby minimizing redundant computations. When patch-level predictions are insufficient, the framework intelligently leverages coarse global predictions to refine results. Evaluated across 3 tasks using 3 segmentation backbones, NMSW achieves competitive accuracy compared to SW inference while significantly reducing computational complexity by 91% (88.0 to 8.00 TMACs). Moreover, it delivers a 9.1x faster inference on the H100 GPU (99.0 to 8.3 sec) and a 11.1x faster inference on the Xeon Gold CPU (2110 to 189 sec). NMSW is model-agnostic, further boosting efficiency when integrated with any existing efficient segmentation backbones. The code is avaialble: https://github.com/Youngseok0001/open_nmsw. |
| title | No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.10814 |