A multi-organ point cloud registration algorithm for abdominal CT registration

Samuel Joutard, Thomas Pheiffer, Chloe Audigier, Patrick Wohlfahrt, Reuben Dorent, Sebastien Piat, Markus Juergens, Tom Vercauteren, Marc Modat, Tommaso Mansi

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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Abstract

Registering CT images of the chest is a crucial step for several tasks such as disease progression tracking or surgical planning. It is also a challenging step because of the heterogeneous content of the human abdomen which implies complex deformations. In this work, we focus on accurately registering a subset of organs of interest. We register organ surface point clouds, as may typically be extracted from an automatic segmentation pipeline, by expanding the Bayesian Coherent Point Drift algorithm (BCPD).
We introduce MO-BCPD, a multi-organ version of the BCPD algorithm which explicitly models three important aspects of this task: organ individual elastic properties, inter-organ motion coherence and segmentation inaccuracy. This model also provides an interpolation framework to estimate the deformation of the entire volume. We demonstrate the efficiency of our method by registering different patients from the LITS challenge dataset. The target registration error on anatomical landmarks is almost twice as small for MO-BCPD compared to standard BCPD while imposing the same constraints on individual organs deformation.
Original languageEnglish
Title of host publicationBiomedical Image Registration - 10th International Workshop, WBIR 2022, Proceedings
EditorsAlessa Hering, Julia Schnabel, Miaomiao Zhang, Enzo Ferrante, Mattias Heinrich, Daniel Rueckert
Pages75–84
Number of pages10
DOIs
Publication statusPublished - 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13386 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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