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Automatic hippocampus localization in histological images using PSO-based deformable models

Abstract : The Allen Brain Atlas (ABA) is a cellular-resolution, genome-wide map of gene expression in the mouse brain which allows users to compare gene expression patterns in neuroanatomical structures. The correct localization of the structures is the first step to carry on this comparison in an automatic way. In this paper we present a completely automatic tool for the localization of the hippocampus that can be easily adapted also to other subcortical structures. This goal is achieved in two distinct phases. The first phase, called "best reference slice selection", is performed by comparing the image of the brain with a reference Atlas provided by ABA using a two-step affine registration. By doing so the system is able to automatically find to which brain section the image corresponds and wherein the image the hippocampus is roughly located. The second phase, the proper "hippocampus localization", is based on a method that combines Particle Swarm Optimization (PSO) and a novel technique inspired by Active Shape Models (ASMs). The hippocampus is found by adapting a deformable model derived statistically, in order to make it overlap with the hippocampus image. Experiments on a test set of 120 images yielded a perfect or good localization in 89.2% of cases.
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Submitted on : Wednesday, October 28, 2015 - 1:31:55 PM
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Roberto Ugolotti, Pablo Mesejo, Stefano Cagnoni, Mario Giacobini, Ferdinando Di Cunto. Automatic hippocampus localization in histological images using PSO-based deformable models. 13th Genetic and Evolutionary Computation Conference companion (GECCO’11), Jul 2011, Dublin, Ireland. pp.487-494, ⟨10.1145/2001858.2002038⟩. ⟨hal-01221664⟩



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