PI-Att：通过自适应持久同调图像表示实现分割网络的拓扑注意力机制
PI-Att: Topology Attention for Segmentation Networks through Adaptive Persistence Image Representation
AI总结：
针对医学图像分割网络常忽略拓扑正确性的问题，提出一种基于自适应持久同调图像表示的拓扑感知损失PI-Att，可随训练进程自适应调整拓扑学习重点，在两个CT血管分割数据集上验证了有效性。
摘要：
在医学图像中分割多个目标（例如器官）通常需要理解其拓扑结构，拓扑结构可同时量化目标的形状及其相对位置。这种理解对于分割网络在有限训练数据下实现更好的泛化至关重要，而训练数据有限在医学图像分析中十分常见。然而，许多主流网络仅被训练用于优化逐像素性能，忽略了分割结果的拓扑正确性。本文提出了一种新的拓扑感知损失函数，我们称之为PI-Att，它能显式地促使网络最小化真实标注图与预测图之间的拓扑差异。我们首次在分割网络损失的研究语境下，采用持久同调图像表示来量化每张图的拓扑结构。此外，我们提出了一种新机制，可在每个训练轮次结束时根据网络性能自适应计算持久同调图像。这种自适应计算使网络能够在训练初期学习拓扑轮廓，在训练后期学习拓扑细节。我们在两个不同的CT图像主动脉与大血管分割数据集上验证了所提出的PI-Att损失的有效性。
英文摘要：
Segmenting multiple objects (e.g., organs) in medical images often requires an understanding of their topology, which simultaneously quantifies the shape of the objects and their positions relative to each other. This understanding is important for segmentation networks to generalize better with limited training data, which is common in medical image analysis. However, many popular networks were trained to optimize only pixel-wise performance, ignoring the topological correctness of the segmentation. In this paper, we introduce a new topology-aware loss function, which we call PI-Att, that explicitly forces the network to minimize the topological dissimilarity between the ground truth and prediction maps. We quantify the topology of each map by the persistence image representation, for the first time in the context of a segmentation network loss. Besides, we propose a new mechanism to adaptively calculate the persistence image at the end of each epoch based on the network's performance. This adaptive calculation enables the network to learn topology outline in the first epochs, and then topology details towards the end of training. The effectiveness of the proposed PI-Att loss is demonstrated on two different datasets for aorta and great vessel segmentation in computed tomography images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
