{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:04:59Z","timestamp":1782180299703,"version":"3.54.5"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90\u201395%) and overlap (between 90\u201394%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.<\/jats:p>","DOI":"10.3390\/fi13040101","type":"journal-article","created":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T11:21:38Z","timestamp":1618831298000},"page":"101","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Coronary Centerline Extraction from CCTA Using 3D-UNet"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4982-6930","authenticated-orcid":false,"given":"Alexandru","family":"Doroban\u021biu","sequence":"first","affiliation":[{"name":"Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentin","family":"Ogrean","sequence":"additional","affiliation":[{"name":"Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8100-1379","authenticated-orcid":false,"given":"Remus","family":"Brad","sequence":"additional","affiliation":[{"name":"Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, 550024 Sibiu, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1186\/s43044-019-0029-8","article-title":"Detection of positively remodeled coronary artery lesions by multislice CT and its impact on cardiovascular future events","volume":"71","author":"Galal","year":"2019","journal-title":"Egypt Heart J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.15420\/icr.2014.9.3.145","article-title":"Fractional Flow Reserve Derived from Coronary Imaging and Computational Fluid Dynamics","volume":"9","author":"Pantos","year":"2014","journal-title":"Interv. Cardiol. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.clinbiomech.2019.09.003","article-title":"Fractional flow reserve-based 4D hemodynamic simulation of time-resolved blood flow in left anterior descending coronary artery","volume":"70","author":"Zhao","year":"2019","journal-title":"Clin. Biomech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s41235-019-0171-6","article-title":"What do we know about volumetric medical image interpretation? A review of the basic science and medical image perception literatures","volume":"4","author":"Williams","year":"2019","journal-title":"Cogn. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00530-017-0580-7","article-title":"Segmentation of blood vessels using rule-based and machine-learning-based methods: A review","volume":"25","author":"Zhao","year":"2019","journal-title":"Multimed. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, X., Fu, Y., Lin, J., Ji, Y., Fang, Y., and Wu, J. (2020). Coronary Artery Disease Detection by Machine Learning with Coronary Bifurcation Features. Appl. Sci., 10.","DOI":"10.3390\/app10217656"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Danilov, A., Pryamonosov, R., and Yurova, A. (2016). Image Segmentation for Cardiovascular Biomedical Applications at Different Scales. Computation, 4.","DOI":"10.3390\/computation4030035"},{"key":"ref_8","unstructured":"Bates, R., Irving, B., Markelc, B., Kaeppler, J., Muschel, R., Grau, V., and Schnabel, J.A. (2017). Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Han, D., Shim, H., Jeon, B., Jang, Y., Hong, Y., Jung, S., Ha, S., and Chang, H.-J. (2016). Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156837"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1007\/s10586-018-2548-6","article-title":"Automatic segmentation of coronary lumen based on minimum path and image fusion from cardiac computed tomography images","volume":"22","author":"Liu","year":"2019","journal-title":"Clust. Comput"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101688","DOI":"10.1016\/j.compmedimag.2019.101688","article-title":"Learning tree-structured representation for 3D coronary artery segmentation","volume":"80","author":"Kong","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"17051","DOI":"10.1007\/s11042-018-7087-x","article-title":"Vessel segmentation using centerline constrained level set method","volume":"78","author":"Lv","year":"2019","journal-title":"Multimed Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1002\/acs.2762","article-title":"Automatic segmentation of coronary tree in CT angiography images","volume":"33","author":"Gao","year":"2019","journal-title":"Int. J. Adapt. Control. Signal. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"170690","DOI":"10.1109\/ACCESS.2019.2955710","article-title":"Extraction Method of Coronary Artery Blood Vessel Centerline in CT Coronary Angiography","volume":"7","author":"Sheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41816","DOI":"10.1109\/ACCESS.2018.2859786","article-title":"Automatic Coronary Centerline Extraction Using Gradient Vector Flow Field and Fast Marching Method From CT Images","volume":"6","author":"Cui","year":"2018","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1007\/978-3-030-20351-1_34","article-title":"DeepCenterline: A Multi-task Fully Convolutional Network for Centerline Extraction","volume":"Volume 11492","author":"Chung","year":"2019","journal-title":"Information Processing in Medical Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.media.2018.10.005","article-title":"Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier","volume":"51","author":"Wolterink","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Vinyals, O., Senior, A., and Sak, H. (2015). Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks. Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Salahuddin, Z., Lenga, M., and Nickisch, H. (2020). Multi-Resolution 3D Convolutional Neural Networks for Automatic Coronary Centerline Extraction in Cardiac CT Angiography Scans. arXiv.","DOI":"10.1109\/ISBI48211.2021.9434002"},{"key":"ref_20","first-page":"24","article-title":"Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction","volume":"Volume 12266","author":"Martel","year":"2020","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2020"},{"key":"ref_21","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1007\/978-3-319-60964-5_58","article-title":"Deep Quantitative Liver Segmentation and Vessel Exclusion to Assist in Liver Assessment","volume":"Volume 723","year":"2017","journal-title":"Medical Image Understanding and Analysis"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","article-title":"DUNet: A deformable network for retinal vessel segmentation","volume":"178","author":"Jin","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_24","unstructured":"Zhuang, J. (2019). LadderNet: Multi-path networks based on U-Net for medical image segmentation. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guo, C., Szemenyei, M., Yi, Y., Wang, W., Chen, B., and Fan, C. (2020). SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation. arXiv.","DOI":"10.1109\/BIBE.2019.00085"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. arXiv.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_27","first-page":"291","article-title":"3D U2-Net: A 3D Universal U-Net for Multi-domain Medical Image Segmentation","volume":"Volume 11765","author":"Shen","year":"2019","journal-title":"Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2018). Focal Loss for Dense Object Detection. arXiv.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.-Y., Song, Y., and Belongie, S. (2019). Class-Balanced Loss Based on Effective Number of Samples. arXiv.","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref_30","unstructured":"(2020, January 11). Rotterdam Coronary Artery Algorithm Evaluation Framework. Available online: http:\/\/coronary.bigr.nl\/centerlines\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1016\/j.media.2009.06.003","article-title":"Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms","volume":"13","author":"Schaap","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_32","unstructured":"(2020, January 11). Rotterdam Coronary Artery Challenge Categories. Available online: http:\/\/coronary.bigr.nl\/centerlines\/about.php."},{"key":"ref_33","unstructured":"(2020, January 11). 3D Slicer. Available online: https:\/\/www.slicer.org\/."},{"key":"ref_34","unstructured":"(2020, January 11). GitHub Slicer. Available online: https:\/\/github.com\/Slicer\/Slicer."},{"key":"ref_35","unstructured":"(2019, May 11). Alexandru Doroban\u021biu\u2014GitHub. Available online: https:\/\/github.com\/AlexDorobantiu."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/3-540-44438-6_31","article-title":"Morphological Operations on 3D and 4D Images: From Shape Primitive Detection to Skeletonization","volume":"Volume 1953","author":"Borgefors","year":"2000","journal-title":"Discrete Geometry for Computer Imagery"},{"key":"ref_37","unstructured":"(2020, August 29). MIC-DKFZ\/batchgenerators. Available online: https:\/\/github.com\/MIC-DKFZ\/batchgenerators."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2018.09.013","article-title":"GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification","volume":"321","author":"Diamant","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_39","unstructured":"(2020, August 29). AlexDorobantiu\/CoronaryCenterlineUnet. Available online: https:\/\/github.com\/AlexDorobantiu\/CoronaryCenterlineUnet."},{"key":"ref_40","unstructured":"Iglovikov, V., and Shvets, A. (2018). TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Manzo, M., and Pellino, S. (2020). Bucket of Deep Transfer Learning Features and Classification Models for Melanoma Detection. J. Imaging, 6.","DOI":"10.3390\/jimaging6120129"},{"key":"ref_42","unstructured":"(2020, September 24). WHO\u2014Cardiovascular diseases (CVDs) Fact Sheet. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cardiovascular-diseases-(cvds)."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/4\/101\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:49:47Z","timestamp":1760161787000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/13\/4\/101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":42,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["fi13040101"],"URL":"https:\/\/doi.org\/10.3390\/fi13040101","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,19]]}}}