Geo-UNet：用于血管内超声中临床级管腔分割的几何约束神经框架
Geo-UNet: A Geometrically Constrained Neural Framework for Clinical-Grade Lumen Segmentation in Intravascular Ultrasound
AI总结：
针对静脉 IVUS 管腔分割精度不足，本文提出几何约束的 Geo-UNet，利用极坐标、双任务预测、CDFeLU 激活和复合损失实现接近临床级的泛化。
摘要：
在血管内超声（IVUS）中精确估计管腔边界，是确定用于治疗深静脉血栓形成（DVT）的介入支架尺寸所必需的。遗憾的是，诸如 UNet 之类的现有分割网络缺乏在 IVUS 工作流程中实现临床应用所需的精度。这是因为难以在考虑 IVUS 成像径向几何的同时，从有限的训练数据中自动学习准确的管腔轮廓。我们提出 Geo-UNet 框架，通过一种由管腔轮廓分割任务的几何特性所启发的设计来解决这些问题。我们首先将输入数据和分割目标从笛卡尔坐标转换为极坐标。从 convUNet 特征提取器出发，我们提出一种双任务设置：一个任务用于常规的逐像素标注，另一个任务用于单边界管腔轮廓定位。我们通过将预测的管腔轮廓传入一种名为 CDFeLU 的新激活函数，直接结合两种预测，以滤除虚假的逐像素预测。我们的统一损失函数谨慎平衡基于区域、基于距离和基于轮廓的惩罚，从而在未见过的患者数据上提供接近临床级的泛化能力。我们还引入了一种轻量级的推理时技术，以增强分割平滑性。我们在一个静脉 IVUS 数据集上对照最先进模型展示了该框架的有效性。
英文摘要：
Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segmentation networks like the UNet lack the precision needed for clinical adoption in IVUS workflows. This arises due to the difficulty of automatically learning accurate lumen contour from limited training data while accounting for the radial geometry of IVUS imaging. We propose the Geo-UNet framework to address these issues via a design informed by the geometry of the lumen contour segmentation task. We first convert the input data and segmentation targets from Cartesian to polar coordinates. Starting from a convUNet feature extractor, we propose a two-task setup, one for conventional pixel-wise labeling and the other for single boundary lumen-contour localization. We directly combine the two predictions by passing the predicted lumen contour through a new activation (named CDFeLU) to filter out spurious pixel-wise predictions. Our unified loss function carefully balances area-based, distance-based, and contour-based penalties to provide near clinical-grade generalization in unseen patient data. We also introduce a lightweight, inference-time technique to enhance segmentation smoothness. The efficacy of our framework on a venous IVUS dataset is shown against state-of-the-art models.
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