Coronary Vessel Segmentation in X-ray Angiography Images using Edge-based Tracking Method

Abstract

Automated extraction of coronary arteries is an essential process in the diagnosis of treatment for coronary artery disease (CAD) with computer assistance. Accurately outlining the coronary artery is difficult when using X-ray coronary angiography (XCA) because of the low signal-to-noise ratio and the presence of interfering background structures. In this paper, a new approach for segmenting vessels in angiograms is presented, specifically designed to tackle the difficulties arising from non-uniform illumination, artifacts, and noise present in angiographic images. The proposed method employs an edge-based tracking tool to generate an initial probability map for segmentation. A segmentation method based on coronary vessel tracking is presented for finding the border and centerline of the vessel. The proposed method is designed based on two main components: preprocessing and tracking. In the preprocessing stage, a guided filter and edge-sharpening algorithms are used to enhance the features of the original image. In the tracking stage, an initial point is selected, and using the Gaussian property, a semi-circle operator is applied to track the line perpendicular to the vessel. The proposed method demonstrated remarkable performance in terms of sensitivity and specificity, achieving values of 86.93 and 99.61, respectively. Additionally, the method achieved an accuracy rate of 97.81. Notably, the proposed method outperformed existing state-of-the-art segmentation methods, as indicated by its higher dice score. These impressive results signify a significant advancement in the field of vessel segmentation, highlighting the effectiveness and superiority of the proposed approach.

Segmentação de Pólipos em Imagens de Colonoscopia utilizando YOLOv8

“…O desempenho deste algoritmo já foi avaliado no processamento de imagens de diversas áreas médicas, obtendo bons resultados em imagens de ultrassom [Inui et al 2023], tomografia computadorizada e raio-X [Pandey et al 2023] e angiografia [Osama et al 2023]. No contexto de pólipos, este algoritmo obteve bons resultados na tarefa de detecc ¸ão de objetos, alcanc ¸ando 0,956 de precision, 0,917 de recall, e 0,924 de dice [Lalinia and Sahafi 2023].…”

“…A escolha deste modelo foi motivada pelo fato de que, apesar de seu desempenho ter sido previamente avaliado na tarefa de detecc ¸ão de pólipos [Lalinia and Sahafi 2023], que visa demarcar esses crescimentos celulares em uma imagem utilizando retângulos delimitadores, existe uma escassez de trabalhos na literatura que avaliem este algoritmo na tarefa de segmentac ¸ão, a qual tem por objetivo realizar a classificac ¸ão à nível de pixel das áreas da imagem correspondentes aos pólipos.…”