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In the current practice of quantitative coronary analysis (QCA), semi\u2010automatic segmentation tools require labor\u2010intensive and time\u2010consuming manual correction, limiting their application in the catheterization room.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to propose rank\u2010based selective ensemble methods that improve the segmentation performance and reduce morphological errors that limit fully automated quantification of coronary artery using deep\u2010learning segmentation of ICA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Two selective ensemble methods proposed in this work integrated the weighted ensemble approach with per\u2010image quality estimation. The segmentation outcomes from five base models with different loss functions were ranked either by mask morphology or estimated dice similarity coefficient (DSC). The final output was determined by imposing different weights according to the ranks. The ranking criteria based on mask morphology were formulated from empirical insight to avoid frequent types of segmentation errors (MSEN), while the estimation of DSCs was performed by comparing the pseudo\u2010ground truth generated from a meta\u2010learner (ESEN). Five\u2010fold cross\u2010validation was performed with the internal dataset of 7426 coronary angiograms from 2924 patients, and prediction model was externally validated with 556 images of 226 patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The selective ensemble methods improved the segmentation performance with DSCs up to 93.07% and provided a better delineation of coronary lesion with local DSCs of up to 93.93%, outperforming all individual models. Proposed methods also minimized the chances of mask disconnection in the most narrowed regions to 2.10%. The robustness of the proposed methods was also evident in the external validation. Inference time for major vessel segmentation was approximately one\u2010sixth of a second.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Proposed methods successfully reduced morphological errors in the predicted masks and were able to enhance the robustness of the automatic segmentation. The results suggest better applicability of real\u2010time QCA\u2010based diagnostic methods in routine clinical settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16554","type":"journal-article","created":{"date-parts":[[2023,6,13]],"date-time":"2023-06-13T12:06:57Z","timestamp":1686658017000},"page":"7822-7839","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Selective ensemble methods for deep learning segmentation of major vessels in invasive coronary angiography"],"prefix":"10.1002","volume":"50","author":[{"given":"Jeeone","family":"Park","sequence":"first","affiliation":[{"name":"Department of Medical Science Asan Medical Institute of Convergence Science and Technology Asan Medical Center University of Ulsan College of Medicine  Seoul South 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