论文标题
学习多级医学图像细分的拓扑相互作用
Learning Topological Interactions for Multi-Class Medical Image Segmentation
论文作者
论文摘要
深度学习方法为多级医学图像细分实现了令人印象深刻的表现。但是，它们的编码不同类别之间拓扑相互作用的能力有限（例如，遏制和排除）。这些约束自然出现在生物医学图像中，对于提高分割质量至关重要。在本文中，我们介绍了一个新型的拓扑交互模块，将拓扑相互作用编码为深神经网络。该实施完全基于卷积，因此非常有效。这使我们有能力将约束结合到端到端培训中，并丰富神经网络的功能表示。该方法的功效在不同类型的相互作用上得到了验证。我们还证明了该方法在2D和3D设置中以及跨越CT和超声等不同方式的专有和公共挑战数据集上的普遍性。代码可在以下网址找到：https：//github.com/topoxlab/topointeraction
Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions among different classes (e.g., containment and exclusion). These constraints naturally arise in biomedical images and can be crucial in improving segmentation quality. In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network. The implementation is completely convolution-based and thus can be very efficient. This empowers us to incorporate the constraints into end-to-end training and enrich the feature representation of neural networks. The efficacy of the proposed method is validated on different types of interactions. We also demonstrate the generalizability of the method on both proprietary and public challenge datasets, in both 2D and 3D settings, as well as across different modalities such as CT and Ultrasound. Code is available at: https://github.com/TopoXLab/TopoInteraction
