Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures

Autor: Lucas Lo Vercio, Ignacio Larrabide, Mirta Mariana del Fresno
Rok vydání: 2019
Předmět:
DEFORMABLE CONTOURS
Adventitia
Support Vector Machine
Jaccard index
Computer science
Health Informatics
purl.org/becyt/ford/2.2 [https]
Pattern Recognition
Automated
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Image Interpretation
Computer-Assisted
Intravascular ultrasound
LUMEN-INTIMA
Image Processing
Computer-Assisted
medicine
Humans
Segmentation
MEDIA-ADVENTITIA
Ultrasonography
Interventional
IVUS
medicine.diagnostic_test
RANDOM FOREST
business.industry
Reproducibility of Results
Pattern recognition
Coronary Vessels
Computer Science Applications
Random forest
Support vector machine
medicine.anatomical_structure
purl.org/becyt/ford/2 [https]
Lumen intima
Artificial intelligence
Artifacts
Tunica Intima
Tunica Media
business
Algorithms
030217 neurology & neurosurgery
Software
Zdroj: CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2019.05.021
Popis: Background: Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall. Methods: Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces. Results: The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ± 0.08 for LI segmentation, compared with 0.88 ± 0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ± 0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ± 0.30. Conclusions: A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements. Fil: Lo Vercio, Lucas. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina Fil: del Fresno, Mirta Mariana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina Fil: Larrabide, Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina
Databáze: OpenAIRE
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