2 research outputs found
A multi-organ point cloud registration algorithm for abdominal CT registration
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.Comment: Accepted at WBIR 202