Automatic multiscale vascular image segmentation algorithm for coronary angiography. (September 2018)

• Record Type: Journal Article
• Authors: Carballal, Adrian; Novoa, Francisco J.; Fernandez-Lozano, Carlos; García-Guimaraes, Marcos; Aldama-López, Guillermo; Calviño-Santos, Ramón; Vazquez-Rodriguez, José Manuel; Pazos, Alejandro
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 1
• Page End: 9
• Publication Date: 2018-09
• Subjects: Multiscale segmentation -- Coronary disease -- Stenotic lesions -- Angiographies segmentation; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.06.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A multiscale-multithread automatic segmentation algorithm is presented. A statistical study that proves the method aptitude for stenotic lession is shown. The algorithm automatically segments any initial angiography image. The algorithm is statistically and significantly better than state-of-the-art. The algorithm shows a suppression of inter- and intra-operator variability. Abstract: Cardiovascular diseases, particularly severe stenosis, are the main cause of death in the western world. The primary method of diagnosis, considered to be the standard in the detection and quantification of stenotic lesions, is a coronary angiography. This article proposes a new automatic multiscale segmentation algorithm for the study of coronary trees that offers results comparable to the best existing semi-automatic method. According to the state-of-the-art, a representative number of coronary angiography images that ensures the generalisation capacity of the algorithm has been used. All these images were selected by clinics from an Haemodynamics Unit. An exhaustive statistical analysis was performed in terms of sensitivity, specificity and Jaccard. Algorithm improvements imply that the clinician can perform tests on the patient and, bypassing the images through the system, can verify, in that moment, the intervention of existing differences in a coronary tree from a previous test, in such a way that it could change its clinical intra-intervention criteria.