通过显式建模位置信息，提升3D医学图像分割的精度与稳定性。
LocBAM: Advancing 3D Patch-Based Image Segmentation by Integrating Location Contex
- 提出新型注意力机制LocBAM，显式融合补丁在全局体积中的位置信息。
- 在低覆盖率情况下性能显著提升，BTCV、AMOS22、KiTS23上平均Dice提高1.2%以上。
- 适合关注空间上下文的3D医学图像分割任务，尤其适用于内存受限场景。
基于补丁的方法广泛用于3D医学图像分割，以应对高分辨率体数据处理中的内存限制。然而，这些方法常忽略补丁在全局体积中的位置信息，当解剖上下文重要时可能影响分割性能。本文研究了位置上下文在基于补丁的3D分割中的作用，提出一种新型注意力机制LocBAM，显式处理空间信息。在BTCV、AMOS22和KiTS23数据集上的实验表明，引入位置上下文可稳定训练并提升分割性能，尤其在补丁-体素覆盖率较低、全局上下文缺失的情况下表现更优。此外，LocBAM在所有测试场景下均优于经典的坐标编码方法CoordConv。代码已公开于https://github.com/compai-lab/2026-ISBI-hooft。
原文摘要 · Abstract (English)
Patch-based methods are widely used in 3D medical image segmentation to address memory constraints in processing high-resolution volumetric data. However, these approaches often neglect the patch's location within the global volume, which can limit segmentation performance when anatomical context is important. In this paper, we investigate the role of location context in patch-based 3D segmentation and propose a novel attention mechanism, LocBAM, that explicitly processes spatial information. Experiments on BTCV, AMOS22, and KiTS23 demonstrate that incorporating location context stabilizes training and improves segmentation performance, particularly under low patch-to-volume coverage where global context is missing. Furthermore, LocBAM consistently outperforms classical coordinate encoding via CoordConv. Code is publicly available at https://github.com/compai-lab/2026-ISBI-hooft
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