A multi-organ point cloud registration algorithm for abdominal CT registration
This work addresses the challenge of multi-organ registration in abdominal CT for tasks like disease tracking, offering a domain-specific improvement over existing methods.
The paper tackled the problem of accurately registering abdominal CT images by developing MO-BCPD, a multi-organ point cloud registration algorithm that models organ properties and segmentation inaccuracies, resulting in a target registration error on anatomical landmarks that is almost twice as small compared to standard BCPD.
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.