A Multi-Object Statistical Atlas Adaptive for Deformable Registration Errors in Anomalous Medical Image Segmentation

Autor(es)

Martins, Samuel Botter ; Spina, Thiago Vallin ; Yasuda, Clarissa ; Falcao, Alexandre X. ; Styner, MA ; Angelini, ED

• Tipo de documento: Artigo Científico
• Fonte: MEDICAL IMAGING 2017: IMAGE PROCESSING; v. 10133, p. 8-pg., 2017-01-01.

Resumo

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. (AU)

• Processo FAPESP: 15/09446-7 - Segmentação de Imagens Médicas: Como integrar modelos de aparência/forma e correção interativa com o mínimo de intervenção do usuário?
• Beneficiário: Thiago Vallin Spina
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 13/07559-3 - Instituto Brasileiro de Neurociência e Neurotecnologia - BRAINN
• Beneficiário: Fernando Cendes
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 16/11853-2 - SAMSAM: Segmentação para Análise e Medidas do Meristema Apical de Plantas
• Beneficiário: Thiago Vallin Spina
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado