While segmentation consists in partitioning a given image into meaningful constituents in order to identify relevant structures such as homogeneous regions or edges, registration, given two images, aims at finding an optimal orientationpreserving one-to-one deformation aligning the structures visible in an image into the corresponding ones in the other. Recently, intertwining both tasks into a single framework has proven to yield better results in terms of accuracy —in particular when the images exhibit weak boundary definition —and increase of reliability of the encoded structure matching —since now, not only based on intensity distribution comparison but also on geometrical and topological features —. In line with this idea, we propose going a step further by adding explicitly some dynamics in the modelling, i.e., by making the minimization problem both space and time-dependent so that the correlation between both tasks is achieved through the process, connecting thus the problem to an interpolation one. The shapes to be matched are viewed as Saint Venant-Kirchhoff materials, a special instance of hyperelastic ones, and are implicitly modelled by level-set functions. These are evolved in order to minimize a functional including both a nonlinear-elasticity-based regularizer prescribing the physical nature of the deformation and a term penalizing the shape misalignment, thus promoting structure matching rather than intensity pairing. Theoretical results emphasizing the mathematical soundness of the model are provided, among which the existence of minimizers and the existence of a weak viscosity solution to the related evolution problem. The model is then applied to the longitudinal registration of hepatic dynamic contrast-enhanced MRI sequences and shows good performance. This application has an important impact on the computer-aided follow-up of patients suffering from liver cancers.

/pdf/a-time-dependent-joint-segmentation-and-registration-model-1tn5mq3pgv.pdf

A Time-Dependent Joint Segmentation and Registration Model: Application to Longitudinal Registration of Hepatic DCE-MRI Sequences

RVXLiverSegmentation is a 3D Slicer (Kikinis et al., 2014, 2022) plug-in aiming at speedingup the annotation of liver anatomy from medical images (CT -Computerized Tomographyscans or MRI -Magnetic Resonance Imagingfor instance). This organ has particular anatomy and physiology; within its parenchymal volume, the liver receives blood from the portal vein and hepatic artery (the former one being the most visible in medical images), and delivers filtered blood through the hepatic veins, toward inferior vena cava. The blood vessels subdivide into the liver as fine vascular tree structures, which makes the segmentation difficult, especially in the case of MRI. To facilitate this task, our plug-in is decomposed into modules dedicated to the segmentation of the liver volume, inner vessels and possible tumors. RVXLiverSegmentation can be downloaded from (Pelletier et al., 2022) or installed from the 3D Slicer software as an official module.

/pdf/the-3d-slicer-rvxliversegmentation-plug-in-for-interactive-3fkoh6ff.pdf

The 3D Slicer RVXLiverSegmentation plug-in for interactive liver anatomy reconstruction from medical images

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Preliminary results with a new annotation tool for liver volume and inner vessels from DCE-MRI data Jonas Lamy, Thibault Pelletier, Guillaume Lienemann, Benoît Magnin, Bertrand Kerautret, Nicolas Passat, Julien Finet, Antoine Vacavant

https://hal.archives-ouvertes.fr/hal-02537919/document

Guillaume Lienemann

Papers

Preliminary results with a new annotation tool for liver volume and inner vessels from DCE-MRI data

A Time-Dependent Joint Segmentation and Registration Model: Application to Longitudinal Registration of Hepatic DCE-MRI Sequences

The 3D Slicer RVXLiverSegmentation plug-in for interactive liver anatomy reconstruction from medical images