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

TL;DR

This paper introduces MO-BCPD, a novel multi-organ registration algorithm for abdominal CT scans that models organ-specific properties and inter-organ coherence, significantly improving registration accuracy over existing methods.

Contribution

The paper presents MO-BCPD, an extension of BCPD that explicitly models multiple organs' elastic properties and inter-organ motion, enhancing registration precision in abdominal CT images.

Findings

Target registration error nearly halved compared to standard BCPD.

Efficient registration demonstrated on LITS dataset.

Improved modeling of organ-specific deformations.

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.