Semi-automatic segmentation of whole-body images in longitudinal studies. (8th December 2020)

• Record Type: Journal Article
• Authors: Grossiord, Eloïse; Risser, Laurent; Kanoun, Salim; Aziza, Richard; Chiron, Harold; Ysebaert, Loïc; Malgouyres, François; Ken, Soléakhéna
• Is Part Of: Biomedical physics & engineering express. Volume 7:Number 1(2021)
• Publication Date: 2020-12-08
• Subjects: image segmentation -- fast marching -- longitudinal segmentation -- whole-body MRI -- clinical trials; Medical physics -- Periodicals; Biophysics -- Periodicals; Biomedical engineering -- Periodicals; Medical sciences -- Periodicals; 610.153
• DOI: 10.1088/2057-1976/abce16
• Languages: English
• ISSNs: 2057-1976

Abstract

Abstract: 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.