A SUPERVOXEL-BASED APPROACH FOR UNSUPERVISED ABNORMAL ASYMMETRY DETECTION IN MR IMAGES OF THE BRAIN

Autor(es)

Martins, Samuel B. ; Ruppert, Guilherme ; Reis, Fabiano ; Yasuda, Clarissa L. ; Falcao, Alexandre X. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019); v. N/A, p. 4-pg., 2019-01-01.

Resumo

Several pathologies are associated with abnormal asymmetries in brain images and their automated detection can improve diagnosis, segmentation, and automatic analysis of abnormal brain tissues (e.g., lesions). In this paper, we introduce a fully unsupervised supervoxel-based approach for abnormal asymmetry detection in MR images of the brain. Also, we present a new method for symmetrical supervoxel extraction called SymmISF. The experiments over a large set of MR-T1 images reveal a higher detection rates and considerably less false positives in comparison to a deep learning auto-encoder approach. (AU)

• 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