Li Xiaosen, Qin Xiao, Huang Chengliang, Lu Yuer, Cheng Jinyan, Wang Liansheng, Liu Ou, Shuai Jianwei, Yuan Chang-An
School of Artificial Intelligence, Guangxi Minzu University, Nanning, 530006, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325105, China.
Guangxi Key Lab of Human-machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530023, China.
Comput Biol Med. 2023 Dec;167:107596. doi: 10.1016/j.compbiomed.2023.107596. Epub 2023 Oct 18.
Organ segmentation in abdominal or thoracic computed tomography (CT) images plays a crucial role in medical diagnosis as it enables doctors to locate and evaluate organ abnormalities quickly, thereby guiding surgical planning, and aiding treatment decision-making. This paper proposes a novel and efficient medical image segmentation method called SUnet for multi-organ segmentation in the abdomen and thorax. SUnet is a fully attention-based neural network. Firstly, an efficient spatial reduction attention (ESRA) module is introduced not only to extract image features better, but also to reduce overall model parameters, and to alleviate overfitting. Secondly, SUnet's multiple attention-based feature fusion module enables effective cross-scale feature integration. Additionally, an enhanced attention gate (EAG) module is considered by using grouped convolution and residual connections, providing richer semantic features. We evaluate the performance of the proposed model on synapse multiple organ segmentation dataset and automated cardiac diagnostic challenge dataset. SUnet achieves an average Dice of 84.29% and 92.25% on these two datasets, respectively, outperforming other models of similar complexity and size, and achieving state-of-the-art results.
腹部或胸部计算机断层扫描（CT）图像中的器官分割在医学诊断中起着至关重要的作用，因为它使医生能够快速定位和评估器官异常，从而指导手术规划并辅助治疗决策。本文提出了一种新颖且高效的医学图像分割方法——SUnet，用于腹部和胸部的多器官分割。SUnet是一个完全基于注意力的神经网络。首先，引入了一种高效的空间缩减注意力（ESRA）模块，不仅能更好地提取图像特征，还能减少整体模型参数并减轻过拟合。其次，SUnet的多个基于注意力的特征融合模块实现了有效的跨尺度特征整合。此外，通过使用分组卷积和残差连接，考虑了一种增强注意力门（EAG）模块，提供更丰富的语义特征。我们在突触多器官分割数据集和自动心脏诊断挑战数据集上评估了所提出模型的性能。SUnet在这两个数据集上分别实现了84.29%和92.25%的平均Dice系数，优于其他具有相似复杂度和规模的模型，并取得了当前最优的结果。
