Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures. In biological structures, however, organs are not separate entities; for example, in reality, a severed vessel is an indication of an underlying problem, but traditional segmentation models are not designed to strictly enforce the continuity of anatomy, potentially leading to inaccurate medical diagnoses. To address this issue, we propose a graph-based approach that enforces the continuity and connectivity of anatomical topology in medical images. Our method encodes the continuity of shapes as a graph constraint, ensuring that the network's predictions maintain this continuity. We evaluate our method on two public benchmarks on retinal vessel segmentation, showing significant improvements in connectivity metrics compared to traditional methods while getting better or on-par performance on segmentation metrics.

翻译：尽管在医学图像分割中默认假定形状连续性与生理解剖结构应被保持，但深度学习方法往往忽略这一点，其研究大多聚焦于将输入数据建模为像素统计而非互联结构。然而在生物结构中，器官并非独立实体——例如，血管断裂在现实中是潜在病变的征兆，但传统分割模型并未被设计为严格保持解剖结构连续性，这可能导致不准确的医学诊断。为解决此问题，我们提出一种基于图的方法，强制保持医学图像中解剖拓扑结构的连续性与连通性。该方法将形状连续性编码为图约束，确保网络预测结果能维持该连续性。我们在两个视网膜血管分割公开基准上评估了该方法，在连通性度量指标上相较于传统方法取得显著提升，同时在分割度量指标上保持更优或持平性能。