A Deep-Discrete Learning Framework for Spherical Surface Registration
This addresses the problem of slow and inaccurate cortical surface alignment in neuroimaging, offering a more efficient and robust solution for researchers and clinicians, though it is incremental as it builds on existing learning-based methods.
The paper tackles cortical surface registration by converting it into a multi-label classification problem using an unsupervised deep learning framework, achieving competitive performance in similarity and areal distortion compared to classical methods and generating smoother deformations than other learning-based approaches.
Cortical surface registration is a fundamental tool for neuroimaging analysis that has been shown to improve the alignment of functional regions relative to volumetric approaches. Classically, image registration is performed by optimizing a complex objective similarity function, leading to long run times. This contributes to a convention for aligning all data to a global average reference frame that poorly reflects the underlying cortical heterogeneity. In this paper, we propose a novel unsupervised learning-based framework that converts registration to a multi-label classification problem, where each point in a low-resolution control grid deforms to one of fixed, finite number of endpoints. This is learned using a spherical geometric deep learning architecture, in an end-to-end unsupervised way, with regularization imposed using a deep Conditional Random Field (CRF). Experiments show that our proposed framework performs competitively, in terms of similarity and areal distortion, relative to the most popular classical surface registration algorithms and generates smoother deformations than other learning-based surface registration methods, even in subjects with atypical cortical morphology.