AI RESEARCH
Train-Free Segmentation in MRI with Cubical Persistent Homology
arXiv CS.LG
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ArXi:2401.01160v3 Announce Type: replace-cross We investigate a framework for train-free MRI segmentation based on Topological Data Analysis. The pipeline proceeds in three steps, first identifying the whole object to segment via automatic thresholding, then detecting a distinctive subset whose topology is known in advance, and finally deducing the various components of the segmentation. A key ingredient is the extraction of approximate representative cycles from persistence diagrams, which provides an interpretable link between persistent features and anatomical components.