Open AccessSemi-automatic segmentation of whole-body images in longitudinal studies
Author(s) -
Éloïse Grossiord,
Laurent Risser,
Salim Kanoun,
R. Aziza,
Harold Chiron,
Loïc Ysebaert,
François Malgouyres,
Soléakhéna Ken
Publication year - 2020
Publication title -
biomedical physics and engineering express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.333
H-Index - 16
ISSN - 2057-1976
DOI - 10.1088/2057-1976/abce16
Subject(s) - segmentation , pipeline (software) , computer science , artificial intelligence , computer vision , pattern recognition (psychology) , programming language
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.