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Investigating the impact of supervoxel segmentation for unsupervised abnormal brain asymmetry detection.
Martins, Samuel B; Telea, Alexandru C; Falcão, Alexandre X.
Afiliação
  • Martins SB; Laboratory of Image Data Science (LIDS), Institute of Computing, University of Campinas, Brazil; Bernoulli Institute, University of Groningen, The Netherlands; Federal Institute of São Paulo, Campinas, Brazil.
  • Telea AC; Department of Information and Computing Sciences, Utrecht University, The Netherlands.
  • Falcão AX; Laboratory of Image Data Science (LIDS), Institute of Computing, University of Campinas, Brazil.
Comput Med Imaging Graph ; 85: 101770, 2020 10.
Article em En | MEDLINE | ID: mdl-32854021
Several brain disorders are associated with abnormal brain asymmetries (asymmetric anomalies). Several computer-based methods aim to detect such anomalies automatically. Recent advances in this area use automatic unsupervised techniques that extract pairs of symmetric supervoxels in the hemispheres, model normal brain asymmetries for each pair from healthy subjects, and treat outliers as anomalies. Yet, there is no deep understanding of the impact of the supervoxel segmentation quality for abnormal asymmetry detection, especially for small anomalies, nor of the added value of using a specialized model for each supervoxel pair instead of a single global appearance model. We aim to answer these questions by a detailed evaluation of different scenarios for supervoxel segmentation and classification for detecting abnormal brain asymmetries. Experimental results on 3D MR-T1 brain images of stroke patients confirm the importance of high-quality supervoxels fit anomalies and the use of a specific classifier for each supervoxel. Next, we present a refinement of the detection method that reduces the number of false-positive supervoxels, thereby making the detection method easier to use for visual inspection and analysis of the found anomalies.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Encéfalo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Comput Med Imaging Graph Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Encéfalo Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Comput Med Imaging Graph Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos