How to Automatically Identify Regions of Interest in High-resolution Images of Lung Biopsy for Interstitial Fibrosis Diagnosis

Autor(es)

Linares, Oscar Cuadros ; Faical, Bruno S. ; Barbosa, Paulo Renato C. ; Hamann, Bernd ; Fabro, Alexandre T. ; 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. 4-pg., 2019-01-01.

Resumo

Airway-centered Interstitial Fibrosis (ACIF) is a histological pattern of Interstitial lung diseases. Its diagnosis requires a multidisciplinary approach, in which diverse information, such as clinical data, computed tomography data, and lung biopsy data, is analyzed. Biopsy samples are digitized at high-resolution. Of crucial interest are broncho- and bronchiolocentric remodeling with extracellular matrix deposition. To analyze an image, specialists have to explore it at low microscope magnification, select a region of interest and export a smaller specified sub-image to be interpreted at higher magnification. This process is performed several times, requiring hours, becoming a tiresome task. We propose a method to support pathologists to identify specific patterns of ACIF in high-resolution images from lung biopsies. This can be done by a) automatic microscope magnification reduction; b) computing the probability of pixels belonging to high-density regions; c) extracting Local Binary Patterns (LBP) of the high- and low-density regions; and d) visualizing them in color. We have evaluated our method on nine high-resolution lung biopsies. We have tested the LBP features of high- and low-density regions with the kNN algorithm and obtained a classification accuracy of 94.4%, which is the highest one reported in the literature for this type of data. (AU)

• 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
• 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: 18/06074-0 - Content-Based Image Retrieval using Selective Visual Attention
• Beneficiário: Oscar Alonso Cuadros Linares
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado