Solving the Cross-Subject Parcel Matching Problem using Optimal Transport
Abstract
Matching structural parcels across different subjects is an open problem in neuroscience. Even when produced by the same technique , parcellations tend to differ in the number, shape, and spatial lo-calization of parcels across subjects. In this work, we propose a parcel matching method based on Optimal Transport. We test its performance by matching parcels of the Desikan atlas, parcels based on a functional criteria and structural parcels. We compare our technique against three other ways to match parcels which are based on the Euclidean distance, the cosine similarity, and the Kullback-Leibler divergence. Our results show that our method achieves the highest number of correct matches.
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