Lunit Inc., Republic of Korea.
School of Software, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 06978, Republic of Korea.
Comput Biol Med. 2022 Aug;147:105782. doi: 10.1016/j.compbiomed.2022.105782. Epub 2022 Jun 21.
Cardiac segmentation of atriums, ventricles, and myocardium in computed tomography (CT) images is an important first-line task for presymptomatic cardiovascular disease diagnosis. In several recent studies, deep learning models have shown significant breakthroughs in medical image segmentation tasks. Unlike other organs such as the lungs and liver, the cardiac organ consists of multiple substructures, i.e., ventricles, atriums, aortas, arteries, veins, and myocardium. These cardiac substructures are proximate to each other and have indiscernible boundaries (i.e., homogeneous intensity values), making it difficult for the segmentation network focus on the boundaries between the substructures.
In this paper, to improve the segmentation accuracy between proximate organs, we introduce a novel model to exploit shape and boundary-aware features. We primarily propose a shape-aware attention module, that exploits distance regression, which can guide the model to focus on the edges between substructures so that it can outperform the conventional contour-based attention method.
In the experiments, we used the Multi-Modality Whole Heart Segmentation dataset that has 20 CT cardiac images for training and validation, and 40 CT cardiac images for testing. The experimental results show that the proposed network produces more accurate results than state-of-the-art networks by improving the Dice similarity coefficient score by 4.97%.
Our proposed shape-aware contour attention mechanism demonstrates that distance transformation and boundary features improve the actual attention map to strengthen the responses in the boundary area. Moreover, our proposed method significantly reduces the false-positive responses of the final output, resulting in accurate segmentation.
心脏在 CT 图像中的心房、心室和心肌分段是进行无症状性心血管疾病诊断的重要一线任务。在最近的几项研究中，深度学习模型在医学图像分割任务中取得了重大突破。与肺部和肝脏等其他器官不同，心脏器官由多个子结构组成，即心室、心房、主动脉、动脉、静脉和心肌。这些心脏子结构彼此相邻，边界难以区分（即，均匀的强度值），使得分割网络难以专注于子结构之间的边界。
为了提高相邻器官之间的分割准确性，我们引入了一种新的模型来利用形状和边界感知特征。我们主要提出了一种形状感知注意模块，利用距离回归，可以引导模型专注于子结构之间的边缘，从而优于传统的基于轮廓的注意方法。
在实验中，我们使用了多模态全心脏分割数据集，该数据集包含 20 张 CT 心脏图像用于训练和验证，以及 40 张 CT 心脏图像用于测试。实验结果表明，与最先进的网络相比，所提出的网络通过提高 Dice 相似性系数得分 4.97%，产生了更准确的结果。
我们提出的形状感知轮廓注意机制表明，距离变换和边界特征可以改善实际注意图，从而增强边界区域的响应。此外，我们提出的方法显著减少了最终输出的假阳性响应，实现了准确的分割。
