Segmenting the organs from abdominal CT images is a vital procedure for computer-aided diagnosis and treatment. Accurate and simultaneous segmentation of multiple abdominal organs remains challenging due to the complex structures, varying sizes, and fuzzy boundaries. Currently, most methods aiming at improving segmentation accuracy involve either deepening the network or employing large-scale models, which results in a heavy computation burden and a huge number of model parameters. It is difficult to deploy these methods in a medical environment. In this paper, we present a lightweight network based on multi-scale context fusion and dual self-attention. The dual self-attention mechanism is used to obtain target organ responses from channel domain, while also strengthening the correlation of global information from spatial domain. Considering the complex structure of abdominal organs, we design a multi-scale context fusion module comprised of a pyramid pooling (PP) and an anisotropic strip pooling (ASP). The PP is used to acquire rich local features by aggregating context information from different receptive fields, while the ASP is designed to extract strip features in different directions to help the network establish long-distance dependencies and capture the characteristics of elongated organs, such as pancreas and spleen. Moreover, a residual module is introduced in the skip connection to learn features related to edges and small objects. The proposed method achieves averaged Dice of 90.1% and 82.5% on the FLARE and BTCV datasets, respectively, with only 6.25M model parameters and 21.40G FLOPs, outperforming many state-of-the-art methods.

中文翻译：

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基于多尺度上下文融合和双重自注意力的腹部多器官分割轻量级网络

从腹部 CT 图像中分割器官是计算机辅助诊断和治疗的重要过程。由于结构复杂、大小不一、边界模糊，多个腹部器官的准确同步分割仍然具有挑战性。目前，大多数旨在提高分割精度的方法要么加深网络，要么采用大规模模型，这导致计算负担重且模型参数数量庞大。在医疗环境中部署这些方法很困难。在本文中，我们提出了一种基于多尺度上下文融合和双重自注意力的轻量级网络。利用双重自注意力机制从通道域获取靶器官反应，同时从空间域加强全局信息的相关性。考虑到腹部器官的复杂结构，我们设计了一个由金字塔池（PP）和各向异性条带池（ASP）组成的多尺度上下文融合模块。 PP用于通过聚合来自不同感受野的上下文信息来获取丰富的局部特征，而ASP旨在提取不同方向的条带特征，以帮助网络建立长距离依赖关系并捕获细长器官（例如胰腺）的特征和脾。此外，在跳跃连接中引入了残差模块来学习与边缘和小物体相关的特征。该方法在 FLARE 和 BTCV 数据集上分别实现了 90.1% 和 82.5% 的平均 Dice，仅需要 6.25M 模型参数和 21.40G FLOPs，优于许多最先进的方法。