Multiple cortical surface correspondence using pairwise shape similarity

Authors:
Pahal Dalal;Feng Shi;Dinggang Shen;Song Wang
Affiliations:
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC;Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC;Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC;Department of Computer Science and Engineering, University of South Carolina, Columbia, SC
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Year:
2010

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Abstract

Accurately corresponding a population of human cortical surfaces provides important shape information for the diagnosis of many brain diseases. This problem is very challenging due to the highly convoluted nature of cortical surfaces. Pairwise methods using a fixed template may not handle well the case when a target cortical surface is substantially different from the template. In this paper, we develop a new method to organize the population of cortical surfaces into pairs with high shape similarity and only correspond such similar pairs to achieve a higher accuracy. In particular, we use the geometric information to identify colocated gyri and sulci for defining a new measure of shape similarity. We conduct experiments on 40 instances of the cortical surface, resulting in an improved performance over several existing shape-correspondence methods.