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

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 Risseret 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.

Minimizing the total variation under a general convex constraint for image restoration.

In this paper, we present a general framework for image restoration; despite its simplicity, certain variational and certain wavelet approaches can be formulated within this framework. This permits the construction of a natural model, with only one parameter, which has the advantages of both approaches. We give a mathematical analysis of this model, describe our algorithm and illustrate this by some experiments.