3DBGrowth: volumetric vertebrae segmentation and reconstruction in magnetic resonance imaging

Autor(es)

Ramos, Jonathan S. ; Cazzolato, Mirela T. ; Faical, Bruno S. ; Nogueira-Barbosa, Marcello H. ; Traina, Caetano, Jr. ; Traina, Agma J. M. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS); v. N/A, p. 6-pg., 2019-01-01.

Resumo

Segmentation of medical images is critical for making several processes of analysis and classification more reliable. With the growing number of people presenting back pain and related problems, the semi-automatic segmentation and 3D reconstruction of vertebral bodies became even more important to support decision making. A 3D reconstruction allows a fast and objective analysis of each vertebrae condition, which may play a major role in surgical planning and evaluation of suitable treatments. In this paper, we propose 3DBGrowth, which develops a 3D reconstruction over the efficient Balanced Growth method for 2D images. We also take advantage of the slope coefficient from the annotation time to reduce the total number of annotated slices, reducing the time spent on manual annotation. We show experimental results on a representative dataset with 17 MRI exams demonstrating that our approach significantly outperforms the competitors and, on average, only 37% of the total slices with vertebral body content must be annotated without losing performance/accuracy. Compared to the state-of-the-art methods, we have achieved a Dice Score gain of over 5% with comparable processing time. Moreover, 3DBGrowth works well with imprecise seed points, which reduces the time spent on manual annotation by the specialist. (AU)

• Processo FAPESP: 18/24414-2 - Ambiente para integração de técnicas para a extração de características e bases de dados complexos para o projeto MIVisBD
• Beneficiário: Mirela Teixeira Cazzolato
• Modalidade de apoio: Bolsas no Brasil - Programa Capacitação - Treinamento Técnico
• Processo FAPESP: 17/23780-2 - Recuperação por conteúdo de imagens médicas para apoio a decisão clínica usando a abordagem radiômica
• Beneficiário: Jonathan da Silva Ramos
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 18/06228-7 - Detecção de padrões e anomalias em dados médicos usando Modelagem Matemática
• Beneficiário: Bruno Squizato Faiçal
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 16/17078-0 - Mineração, indexação e visualização de Big Data no contexto de sistemas de apoio à decisão clínica (MIVisBD)
• Beneficiário: Agma Juci Machado Traina
• Modalidade de apoio: Auxílio à Pesquisa - Temático