Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, China.
Department of Pediatric Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, China.
Sci Rep. 2024 Oct 28;14(1):25704. doi: 10.1038/s41598-024-76234-y.
Accurate medical image segmentation plays a vital role in clinical practice. Convolutional Neural Network and Transformer are mainstream architectures for this task. However, convolutional neural network lacks the ability of modeling global dependency while Transformer cannot extract local details. In this paper, we propose DATTNet, Dual ATTention Network, an encoder-decoder deep learning model for medical image segmentation. DATTNet is exploited in hierarchical fashion with two novel components: (1) Dual Attention module is designed to model global dependency in spatial and channel dimensions. (2) Context Fusion Bridge is presented to remix the feature maps with multiple scales and construct their correlations. The experiments on ACDC, Synapse and Kvasir-SEG datasets are conducted to evaluate the performance of DATTNet. Our proposed model shows superior performance, effectiveness and robustness compared to SOTA methods, with mean Dice Similarity Coefficient scores of 92.2%, 84.5% and 89.1% on cardiac, abdominal organs and gastrointestinal poly segmentation tasks. The quantitative and qualitative results demonstrate that our proposed DATTNet attains favorable capability across different modalities (MRI, CT, and endoscopy) and can be generalized to various tasks. Therefore, it is envisaged as being potential for practicable clinical applications. The code has been released on https://github.com/MhZhang123/DATTNet/tree/main .
准确的医学图像分割在临床实践中起着至关重要的作用。卷积神经网络和 Transformer 是该任务的主流架构。然而，卷积神经网络缺乏建模全局依赖的能力，而 Transformer 则无法提取局部细节。在本文中，我们提出了 DATTNet，一种用于医学图像分割的编解码器深度学习模型。DATTNet 以分层的方式利用两个新颖的组件：（1）双注意力模块，用于在空间和通道维度上建模全局依赖关系。（2）上下文融合桥，用于混合多尺度的特征图并构建它们的相关性。在 ACDC、Synapse 和 Kvasir-SEG 数据集上进行的实验评估了 DATTNet 的性能。与 SOTA 方法相比，我们提出的模型在心脏、腹部器官和胃肠道分段任务上的平均骰子相似系数得分分别为 92.2%、84.5%和 89.1%，具有更好的性能、有效性和鲁棒性。定量和定性结果表明，我们提出的 DATTNet 在不同模态（MRI、CT 和内窥镜）上具有良好的性能，可以推广到各种任务。因此，它有望在实际的临床应用中得到应用。代码已在 https://github.com/MhZhang123/DATTNet/tree/main 上发布。
