Semi-automatic segmentation of whole-body images in longitudinal studies

Eloïse Grossiord (1) , Laurent Risser (1) , Salim Kanoun (2) , Richard Aziza , Harold Chiron , Loïc Ysebaert , François Malgouyres , Soléakhéna Ken (2)

1 IMT - Institut de Mathématiques de Toulouse UMR5219
2 Institut Claudius Regaud

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.

Mots clés: image segmentation; fast marching; longitudinal segmentation; whole-body MRI; clinical trials

Domaines: Hématologie

Eloïse Grossiord, Laurent Risser, Salim Kanoun, Richard Aziza, Harold Chiron, et al.. Semi-automatic segmentation of whole-body images in longitudinal studies. Biomedical Physics & Engineering Express, 2020, 7, pp.015014. ⟨10.1088/2057-1976/abce16⟩. ⟨hal-03234347⟩