Efficient Segmentation Pipeline Using Diffeomorphic Image Registration: A Validation Study

Abstract

Functional measurements of the left atrium (LA) in atrial fibrillation (AF) patients is limited to a single CINE slice midway through the LA. Nonetheless, a full 3D characterization of atrial functional measurements would provide more insights into LA function. But this improved modeling capacity comes at a price of requiring LA segmentation of each 3D time point,a time-consuming and expensive task that requires anatomy-specific expertise.We propose an efficient pipeline which requires ground truth segmentation of a single (or limited) CINE time point to accurately propagate it throughout the sequence. This method significantly saves human effort and enable better characterization of LA anatomy. From a gated cardiac CINE MRI sequence we select a single CINE time point with ground truth segmentation, and assuming cyclic motion, we register other images corresponding to all time points using diffeomorphic registration in ANTs. The diffeomorphic registration fields allow us to map a given anatomical shape (segmentation) to each CINE time point, facilitating the construction of a 4D shape model.

Learning spatiotemporal statistical shape models for non-linear dynamic anatomies

“…The segmentation was then transformed to each time point through pairwise deformable registrations to create a full 3D segmentation for each time point Morris et al (2020). This technique reduces the manual burden and has been shown to produce segmentations with a reasonable error when compared to fully manual segmentation Parikh et al (2019). An example of one subject left atrium mesh sequence is provided in Figure 5.…”