We propose a semi-automatic segmentation pipeline designed for longitudinal studies considering structures with large anatomical variability, where expert interactions are required for relevant segmentations. Our pipeline builds on the regularized Fast Marching (rFM) segmentation approach by Risser et al (2018). It consists in transporting baseline multi-label FM seeds on follow-up images, selecting the relevant ones and finally performing the rFM approach. It showed increased, robust and faster results compared to clinical manual segmentation. Our method was evaluated on 3D synthetic images and patients’ whole-body MRI. It allowed a robust and flexible handling of organs longitudinal deformations while considerably reducing manual interventions.

我们提出了一种半自动分割管道，设计用于纵向研究，考虑具有大解剖变异性的结构，其中相关分割需要专家交互。我们的管道建立在 Risser等人(2018)的正则化快速行进 (rFM) 分割方法之上。它包括在后续图像上传输基线多标签 FM 种子，选择相关的种子，最后执行 rFM 方法。与临床手动分割相比，它显示出增加、稳健和更快的结果。我们的方法在 3D 合成图像和患者的全身 MRI 上进行了评估。它允许对器官纵向变形进行稳健而灵活的处理，同时大大减少人工干预。