A robust fully automatic lumen segmentation method for in vivo intracoronary optical coherence tomography

• Autor(a) principal: Macedo,Maysa Malfiza Garcia de
• Data de Publicação: 2016
• Outros Autores: Takimura,Celso Kiyoshi, Lemos,Pedro Alves, Gutierrez,Marco Antonio
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research on Biomedical Engineering (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2446-47402016000100035
• topic: Optical coherence tomography; Intravascular; Computer vision; Lumen segmentation; Complex plaque; Atherosclerosis

Abstract Introduction: Intravascular optical coherence tomography (IVOCT) is an in-vivo imaging modality based on the introduction of a catheter in a blood vessel for viewing its inner wall using electromagnetic radiation. One of the most developed automatic applications for this modality is the lumen area segmentation, however on the evaluation of these methods, the slices inside bifurcation regions, or with the presence of complex atherosclerotic plaques and dissections are usually discarded. This paper describes a fully-automatic method for computing the lumen area in IVOCT images where the set of slices includes complex atherosclerotic plaques and dissections. Methods The proposed lumen segmentation method is divided into two steps: preprocessing, including the removal of artifacts and the second step comprises a lumen detection using morphological operations. In addition, it is proposed an approach to delimit the lumen area for slices inside bifurcation region, considering only the main branch. Results Evaluation of the automatic lumen segmentation used manual segmentations as a reference, it was performed on 1328 human IVOCT images, presenting a mean difference in lumen area and Dice metrics of 0.19 mm2 and 97% for slices outside the bifurcation, 1.2 mm2 and 88% in the regions with bifurcation without automatic contour correction and 0.52 mm2 and 90% inside bifurcation region with automatic contour correction. Conclusion This present study shows a robust lumen segmentation method for vessel cross-sections with dissections and complex plaque and bifurcation avoiding the exclusion of such regions from the dataset analysis.