Baxter John S H, Inoue Jiro, Drangova Maria, Peters Terry M
Western University, Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada; Western University, Biomedical Engineering Graduate Program, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada.
Western University , Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada.
J Med Imaging (Bellingham). 2016 Oct;3(4):044005. doi: 10.1117/1.JMI.3.4.044005. Epub 2016 Dec 20.
Optimization-based segmentation approaches deriving from discrete graph-cuts and continuous max-flow have become increasingly nuanced, allowing for topological and geometric constraints on the resulting segmentation while retaining global optimality. However, these two considerations, topological and geometric, have yet to be combined in a unified manner. The concept of "shape complexes," which combine geodesic star convexity with extendable continuous max-flow solvers, is presented. These shape complexes allow more complicated shapes to be created through the use of multiple labels and super-labels, with geodesic star convexity governed by a topological ordering. These problems can be optimized using extendable continuous max-flow solvers. Previous approaches required computationally expensive coordinate system warping, which are ill-defined and ambiguous in the general case. These shape complexes are demonstrated in a set of synthetic images as well as vessel segmentation in ultrasound, valve segmentation in ultrasound, and atrial wall segmentation from contrast-enhanced CT. Shape complexes represent an extendable tool alongside other continuous max-flow methods that may be suitable for a wide range of medical image segmentation problems.
源自离散图割和连续最大流的基于优化的分割方法已变得越来越细致入微，在保持全局最优性的同时，允许对所得分割结果施加拓扑和几何约束。然而，拓扑和几何这两个考量因素尚未以统一的方式结合起来。本文提出了“形状复合体”的概念，它将测地星凸性与可扩展的连续最大流求解器相结合。这些形状复合体允许通过使用多个标签和超标签来创建更复杂的形状，其测地星凸性由拓扑排序控制。这些问题可以使用可扩展的连续最大流求解器进行优化。先前的方法需要计算成本高昂的坐标系扭曲，而在一般情况下，这种扭曲定义不明确且含糊不清。这些形状复合体在一组合成图像以及超声血管分割、超声瓣膜分割和对比增强CT心房壁分割中得到了验证。形状复合体是一种可扩展的工具，与其他连续最大流方法一起，可能适用于广泛的医学图像分割问题。
