通过形状感知轮廓注意力实现CT图像心脏分割
Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions
- Lunit Inc.（卢尼特公司）
- School of Software, Soongsil University（崇实大学软件学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对CT图像中邻近心脏子结构边界难以区分的问题，提出形状感知注意力模块，利用距离回归强化边界特征，在Multi-Modality Whole Heart Segmentation数据集上将Dice相似系数提升4.97%，实现更准确的心脏分割。
AI中文摘要：
在计算机断层扫描（CT）图像中对心房、心室和心肌进行心脏分割，是症状前心血管疾病诊断的重要一线任务。在近期的多项研究中，深度学习模型已在医学图像分割任务中展现出显著突破。与肺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邻近，且边界难以辨别（即具有同质强度值），使得分割网络难以聚焦于子结构之间的边界。本文为了提高邻近器官之间的分割精度，提出了一种利用形状和边界感知特征的新模型。我们主要提出了一个形状感知注意力模块，该模块利用距离回归，能够引导模型聚焦于子结构之间的边缘，从而优于传统的基于轮廓的注意力方法。在实验中，我们使用了Multi-Modality Whole Heart Segmentation数据集，该数据集包含20张用于训练和验证的CT心脏图像，以及40张用于测试的CT心脏图像。实验结果表明，所提出的网络将Dice相似系数得分提高了4.97%，比最先进的网络产生了更准确的结果。我们提出的形状感知轮廓注意力机制表明，距离变换和边界特征改善了实际注意力图，从而增强了边界区域的响应。此外，我们提出的方法显著减少了最终输出的假阳性响应，从而实现了准确分割。
英文摘要：
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.
