ArticleMachine learning in medical imaging. MLMI (Workshop)2025
Pseudo-Rendering for Resolution and Topology-Invariant Cortical Parcellation.
Article in Machine learning in medical imaging. MLMI (Workshop), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Parcellation of mesh models for cortical analysis is a central problem in neuroimaging. Most classical and deep learning methods have requisites in terms of mesh topology, requiring inputs that are homeomorphic to a sphere (i.e., no holes or handles). Topology correction algorithms do exist, but their computational complexity is quadratic with the size of the topological defects - sometimes hours, effectively precluding segmentation of incorrect meshes, including those derived from imperfect segmentations or obtained from inherently noisy modalities like surface scanning. Furthermore, deep learning mesh segmentation also struggles surface scans of brains because they are relatively nondescript and require modeling of longer-range dependencies. Here we propose "pseudo-render-inverse-render" (PRIR), a novel perspective on cortical mesh parcellation that effectively reframes the problem as a 2D segmentation task using an direct-inverse rendering framework. Our approach:
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