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Proceedings Paper

A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation
Author(s): Samuel Botter Martins; Thiago Vallin Spina; Clarissa Yasuda; Alexandre X. Falcão
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Paper Abstract

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332G (24 February 2017); doi: 10.1117/12.2254477
Show Author Affiliations
Samuel Botter Martins, Univ. Estadual de Campinas (Brazil)
Federal Institute of Education, Science, and Technology (Brazil)
Thiago Vallin Spina, Univ. Estadual de Campinas (Brazil)
Clarissa Yasuda, Univ. Estadual de Campinas (Brazil)
Alexandre X. Falcão, Univ. Estadual de Campinas (Brazil)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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