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Updated: Aug 17, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Development of deep learning segmentation models for coronary X-ray angiography: Quality assessment by a new global
Miguel Nobre Menezes1, João Lourenço-Silva2, Beatriz Silva1
1Structural and Coronary Heart Disease Unit, Cardiovascular Center of the University of Lisbon, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal; Serviço de Cardiologia, Departamento de Coração e Vasos, CHULN Hospital de Santa Maria, Lisboa, Portugal.
Introduction And Objectives:
Although automatic artificial intelligence (AI) coronary angiography (CAG) segmentation is arguably the first step toward future clinical application, it is underexplored. We aimed to (1) develop AI models for CAG segmentation and (2) assess the results using similarity scores and a set of criteria defined by expert physicians.
Methods:
Patients undergoing CAG were randomly selected in a retrospective study at a single center. Per incidence, an ideal frame was segmented, forming a baseline human dataset (BH), used for training a baseline AI model (BAI). Enhanced human segmentation (EH) was created by combining the best of both. An enhanced AI model (EAI) was trained using the EH. Results were assessed by experts using 11 weighted criteria, combined into a Global Segmentation Score (GSS: 0-100 points). Generalized Dice Score (GDS) and Dice Similarity Coefficient (DSC) were also used for AI models assessment.
Results:
1664 processed images were generated. GSS for BH, EH, BAI and EAI were 96.9+/-5.7; 98.9+/-3.1; 86.1+/-10.1 and 90+/-7.6, respectively (95% confidence interval, p<0.001 for both paired and global differences). The GDS for the BAI and EAI was 0.9234±0.0361 and 0.9348±0.0284, respectively. The DSC for the coronary tree was 0.8904±0.0464 and 0.9134±0.0410 for the BAI and EAI, respectively. The EAI outperformed the BAI in all coronary segmentation tasks, but performed less well in some catheter segmentation tasks.
Conclusions:
We successfully developed AI models capable of CAG segmentation, with good performance as assessed by all scores.
