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

翻译：基于图像块的方法被广泛应用于三维医学图像分割，以应对处理高分辨率体数据时的内存限制。然而，这些方法通常忽略了图像块在全局体数据中的位置信息，当解剖学上下文至关重要时，这会限制分割性能。本文研究了位置上下文在基于图像块的三维分割中的作用，并提出了一种新颖的注意力机制——LocBAM，它能够显式地处理空间信息。在BTCV、AMOS22和KiTS23数据集上的实验表明，整合位置上下文能够稳定训练并提升分割性能，尤其是在图像块对体积覆盖度较低、全局上下文缺失的情况下。此外，LocBAM在性能上持续优于通过CoordConv实现的经典坐标编码方法。代码已在 https://github.com/compai-lab/2026-ISBI-hooft 公开。