Abstract
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 this https URL.
Abstract (translated)
深度学习已经成为医疗图像分割的既定方法，其中3D分割模型在捕捉复杂3D结构方面表现出色，而2D模型则具有高计算效率。然而，分割2.5D图像（具有高平面内分辨率但低通过平面分辨率）是一个相对较未探索的挑战。虽然将2D模型应用于2.5D图像的单个切片是可行的，但它无法捕捉切片之间的空间关系。另一方面，3D模型面临分辨率不一致、计算复杂度以及训练数据有限时过拟合的挑战。在这种情况下，使用仅基于2D神经网络的2.5D模型作为一种有前景的解决方案，因为它们具有较低的计算需求和易于实现的简单性。在本文中，我们介绍了CSA-Net，一种灵活的2.5D分割模型，可以通过创新的自注意力（CSA）模块处理任意数量的切片。该模块利用跨切片关注机制有效地捕捉3D空间信息，通过学习中心切片（用于分割）及其相邻切片之间的长距离依赖关系。此外，CSA-Net还利用自注意力机制来理解中心切片内像素之间的相关性。我们在三个2.5D分割任务上评估了CSA-Net：（1）多分类脑部MRI分割，（2）二分类前列腺MRI分割，（3）多分类前列腺MRI分割。CSA-Net在所有三个任务上都超过了领先的2D和2.5D分割方法，证明了其有效性和优越性。我们的代码可在此https:// URL上公开获取。
URL
https://arxiv.org/abs/2405.00130
