{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:52:54Z","timestamp":1785412374432,"version":"3.56.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["HR20C0026"],"award-info":[{"award-number":["HR20C0026"]}],"id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"award":["HR20C0026"],"award-info":[{"award-number":["HR20C0026"]}],"id":[{"id":"https:\/\/ror.org\/00fdzyk40","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Computed tomography angiography (CTA)-based segmentation of the superficial temporal arteries (STAs) and facial vessels (FVs) is important for neurosurgical and reconstructive planning. Nevertheless, segmentation of STAs and FVs remains challenging because of their small caliber, tortuous courses, and proximity to high-intensity bony structures. This study aims to develop a deep learning framework for accurate automated segmentation of these craniofacial vessels. A single-input 3D nnU-Net v2 model was trained using raw CTA volumes, while a Fusion-based Vesselness Map (FVM) was constructed from multiscale vessel-enhancement filters to emphasize small vascular structures and suppress irrelevant regions such as the skull and skin. Instead of being used as an additional input channel, the FVM was incorporated into the loss function as a spatial prior to guide the network toward vessel boundaries and distal branches. In 72 clinical cases, the FVM-guided model improved segmentation accuracy compared with a baseline model trained with Dice Focal Loss, particularly in boundary delineation. For the STAs, the Average Symmetric Surface Distance decreased from 6.543 mm to 2.941 mm. Qualitative evaluation further showed reduced segmentation noise and fewer false positives near bone and distal branches. These findings suggest that integrating classical vessel enhancement into deep learning supervision can improve morphologically consistent craniofacial vessel segmentation and support preoperative surgical planning.<\/jats:p>","DOI":"10.3390\/bioengineering13070728","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:02:56Z","timestamp":1783436576000},"page":"728","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3551-2053","authenticated-orcid":false,"given":"Hyeonju","family":"Park","sequence":"first","affiliation":[{"name":"SKIA Inc., 1502, 288 Digital-ro, Guro-gu, Seoul 08390, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Chul","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery, Asan Medical Center, Ulsan University College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2950-374X","authenticated-orcid":false,"given":"Kyoyeong","family":"Koo","sequence":"additional","affiliation":[{"name":"SKIA Inc., 1502, 288 Digital-ro, Guro-gu, Seoul 08390, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangyun","family":"Kang","sequence":"additional","affiliation":[{"name":"SKIA Inc., 1502, 288 Digital-ro, Guro-gu, Seoul 08390, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Woo","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Plastic and Reconstructive Surgery, Asan Medical Center, Ulsan University College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6399-4968","authenticated-orcid":false,"given":"Chan-Ung","family":"Park","sequence":"additional","affiliation":[{"name":"SKIA Inc., 1502, 288 Digital-ro, Guro-gu, Seoul 08390, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1093\/asj\/sjy287","article-title":"The Superficial Temporal Artery: Anatomical Map for Facial Reconstruction and Aesthetic Procedures","volume":"39","author":"Koziej","year":"2019","journal-title":"Aesthetic Surg. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1002\/ca.23550","article-title":"The superficial temporal artery: A meta-analysis of its prevalence and morphology","volume":"33","author":"Koziej","year":"2020","journal-title":"Clin. Anat."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1097\/00006123-199702000-00017","article-title":"Surgical Treatment for Pediatric Moyamoya Disease: Use of the Superficial Temporal Artery for Both Areas Supplied by the Anterior and Middle Cerebral Arteries","volume":"40","author":"Suzuki","year":"1997","journal-title":"Neurosurgery"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1093\/asj\/sjz028","article-title":"Anatomical Map of the Facial Artery for Facial Reconstruction and Aesthetic Procedures","volume":"39","author":"Koziej","year":"2019","journal-title":"Aesthetic Surg. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"016103","DOI":"10.1063\/5.0181281","article-title":"A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization","volume":"8","author":"Nannini","year":"2024","journal-title":"APL Bioeng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kurose, Y., Chiba, H., Gu, L., Iho, J., Tokunaga, Y., Horie, M., Nishizawa, K., Hayashi, Y., Koyama, Y., and Harada, T. (2025). Coronary Artery Segmentation with Partial Annotations in Coronary CT Angiography Images. 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), IEEE.","DOI":"10.1109\/ISBI60581.2025.10980766"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kim, H., Jeong, M.J., Koo, K.Y., Choi, Y.J., Heo, E.S., Nam, W., Kang, S., Lee, U.-Y., Kim, Y.-S., and Lee, K.H. (2026). Deep learning accurately and reliably segments pelvic vascular structure in CT scans of gynecologic cancer patients. Front. Oncol., 16.","DOI":"10.3389\/fonc.2026.1687859"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fotin, V.S., Reeves, P.A., Cham, D.M., Henschke, I.C., and Yankelevitz, F.D. (2007). Segmentation of coronary arteries from CT angiography images. Medical Imaging 2007: Computer-Aided Diagnosis, SPIE.","DOI":"10.1117\/12.707810"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"345968","DOI":"10.1155\/2013\/345968","article-title":"Ultrasound Common Carotid Artery Segmentation Based on Active Shape Model","volume":"2013","author":"Yang","year":"2013","journal-title":"Comput. Math. Methods Med."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, H., Seo, K.-H., Kim, K., and Lee, Y. (2025). Optimization of the U-Net++ model for cerebral artery segmentation based on deep learning in computed tomographic angiography images. Medical Imaging 2025: Physics of Medical Imaging, SPIE.","DOI":"10.1117\/12.3046622"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e190178","DOI":"10.1148\/ryai.2020190178","article-title":"Cerebral Artery and Vein Segmentation in Four-dimensional CT Angiography Using Convolutional Neural Networks","volume":"2","author":"Meijs","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"9099","DOI":"10.1007\/s00330-023-09904-6","article-title":"Focused view CT angiography for selective visualization of stroke related arteries: Technical feasibility","volume":"33","author":"Roest","year":"2023","journal-title":"Eur. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhong, H., Zhang, J., and Zhao, L. (2025). MedSAM\/MedSAM2 Feature Fusion: Enhancing nnUNet for 2D TOF-MRA Brain Vessel Segmentation. J. Imaging, 11.","DOI":"10.3390\/jimaging11060202"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Survarachakan, S., Pelanis, E., Khan, Z.A., Kumar, R.P., Edwin, B., and Lindseth, F. (2021). Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation. Electronics, 10.","DOI":"10.3390\/electronics10101165"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1007\/s11548-022-02655-7","article-title":"Vessel filtering and segmentation of coronary CT angiographic images","volume":"17","author":"Huang","year":"2022","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_17","unstructured":"Chen, Y.-C., Lin, Y.-C., Wang, C.-P., Lee, C.-Y., Lee, W.-J., Wang, T.-D., and Chen, C.-M. (2019). Coronary Artery Segmentation in Cardiac CT Angiography Using 3D Multi-Channel U-net. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Frangi, F.A., Niessen, J.W., Vincken, L.K., and Viergever, A.M. (1998). Multiscale vessel enhancement filtering. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/BFb0056195"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Jerman, T., Pernu\u0161, F., Likar, B., and \u0160piclin, \u017d. (2015). Beyond Frangi: An improved multiscale vesselness filter. Medical Imaging 2015: Image Processing, SPIE.","DOI":"10.1117\/12.2081147"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e230024","DOI":"10.1148\/ryai.230024","article-title":"TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images","volume":"5","author":"Wasserthal","year":"2023","journal-title":"Radiol. Artif. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TIP.2013.2293423","article-title":"Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index","volume":"23","author":"Xue","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1080\/2151237X.2007.10129236","article-title":"Adaptive Thresholding using the Integral Image","volume":"12","author":"Bradley","year":"2007","journal-title":"J. Graph. Tools"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D Slicer as an image computing platform for the Quantitative Imaging Network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ciecholewski, M., and Kassja\u0144ski, M. (2021). Computational Methods for Liver Vessel Segmentation in Medical Imaging: A Review. Sensors, 21.","DOI":"10.3390\/s21062027"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109072","DOI":"10.1016\/j.cmpb.2025.109072","article-title":"Deep vessel segmentation with U-Net and texture representation of image (TRI) features provides a foundation for improved objective and automated analysis of coronary artery disease from angiography","volume":"272","author":"Shariaty","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/34.232073","article-title":"Comparing images using the Hausdorff distance","volume":"15","author":"Huttenlocher","year":"1993","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mayerich, D., Bjornsson, C., Taylor, J., and Roysam, B. (2012). NetMets: Software for quantifying and visualizing errors in biological network segmentation. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-S8-S7"},{"key":"ref_29","unstructured":"Powers, D.M.W. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Shit, S., Paetzold, J.C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J.P.W., Bauer, U., and Menze, B.H. (2021). clDice\u2014A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR46437.2021.01629"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","article-title":"Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation","volume":"39","author":"Mehrtash","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Philps, B., Hernandez, V.C.D.M., and Llinares, B.M. (2023). Proper Scoring Loss Functions Are Simple and Effective for Uncertainty Quantification of White Matter Hyperintensities. International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, Springer Nature. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-44336-7_21"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1007\/s10278-022-00735-3","article-title":"Calibrating the Dice Loss to Handle Neural Network Overconfidence for Biomedical Image Segmentation","volume":"36","author":"Yeung","year":"2022","journal-title":"J. Digit. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wolterink, M.J., Leiner, T., and I\u0161gum, I. (2019). Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography. International Workshop on Graph Learning in Medical Imaging, Springer International Publishing. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-35817-4_8"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhai, Z., Staring, M., and Stoel, C.B. (2016). Lung vessel segmentation in CT images using graph-cuts. Medical Imaging 2016: Image Processing, SPIE.","DOI":"10.1117\/12.2216827"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e34501","DOI":"10.2196\/34501","article-title":"Augmented Reality in Vascular and Endovascular Surgery: Scoping Review","volume":"10","author":"Eves","year":"2022","journal-title":"JMIR Serious Games"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.jcms.2023.12.009","article-title":"Application of augmented reality using automatic markerless registration for facial plastic and reconstructive surgery","volume":"52","author":"Kim","year":"2024","journal-title":"J. Cranio-Maxillofac. Surg."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/7\/728\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:39:29Z","timestamp":1783438769000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/7\/728"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,7]]}},"alternative-id":["bioengineering13070728"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13070728","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,24]]}}}