  Deep learning has become the de facto method for medical image segmentation,
with 3D segmentation models excelling in capturing complex 3D structures and 2D
models offering high computational efficiency. However, segmenting 2.5D images,
which have high in-plane but low through-plane resolution, is a relatively
unexplored challenge. While applying 2D models to individual slices of a 2.5D
image is feasible, it fails to capture the spatial relationships between
slices. On the other hand, 3D models face challenges such as resolution
inconsistencies in 2.5D images, along with computational complexity and
susceptibility to overfitting when trained with limited data. In this context,
2.5D models, which capture inter-slice correlations using only 2D neural
networks, emerge as a promising solution due to their reduced computational
demand and simplicity in implementation. In this paper, we introduce CSA-Net, a
flexible 2.5D segmentation model capable of processing 2.5D images with an
arbitrary number of slices through an innovative Cross-Slice Attention (CSA)
module. This module uses the cross-slice attention mechanism to effectively
capture 3D spatial information by learning long-range dependencies between the
center slice (for segmentation) and its neighboring slices. Moreover, CSA-Net
utilizes the self-attention mechanism to understand correlations among pixels
within the center slice. We evaluated CSA-Net on three 2.5D segmentation tasks:
(1) multi-class brain MRI segmentation, (2) binary prostate MRI segmentation,
and (3) multi-class prostate MRI segmentation. CSA-Net outperformed leading 2D
and 2.5D segmentation methods across all three tasks, demonstrating its
efficacy and superiority. Our code is publicly available at
https://github.com/mirthAI/CSA-Net.
