{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:54:29Z","timestamp":1779382469936,"version":"3.53.1"},"reference-count":57,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T00:00:00Z","timestamp":1749600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Coronary artery disease (CAD) is a highly prevalent cardiovascular disease and one of the leading causes of death worldwide. The accurate segmentation of coronary arteries from CT angiography (CTA) images is essential for the diagnosis and treatment of coronary artery disease. However, due to small vessel diameters, large morphological variations, low contrast, and motion artifacts, conventional segmentation methods, including classical image processing (such as region growing and level sets) and early deep learning models with limited receptive fields, are unsatisfactory. We propose SADiff, a hybrid framework that integrates a dilated attention network (DAN) for ROI extraction, a diffusion-based subnet for noise suppression in low-contrast regions, and a striped attention network (SAN) to refine tubular structures affected by morphological variations. Experiments on the public ImageCAS dataset show that it has a Dice score of 83.48% and a Hausdorff distance of 19.43 mm, which is 6.57% higher than U-Net3D in terms of Dice. The cross-dataset validation on the private ImageLaPP dataset verifies its generalizability with a Dice score of 79.42%. This comprehensive evaluation demonstrates that SADiff provides a more efficient and versatile method for coronary segmentation and shows great potential for improving the diagnosis and treatment of CAD.<\/jats:p>","DOI":"10.3390\/jimaging11060192","type":"journal-article","created":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T03:44:58Z","timestamp":1749613498000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["SADiff: Coronary Artery Segmentation in CT Angiography Using Spatial Attention and Diffusion Model"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4354-642X","authenticated-orcid":false,"given":"Ruoxuan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longhui","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianru","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9232-5392","authenticated-orcid":false,"given":"Yuanquan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology (HeBUT), Tianjin 300401, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1093\/eurjpc\/zwab213","article-title":"Burden of ischemic heart disease and its attributable risk factors in 204 countries and territories, 1990\u20132019","volume":"29","author":"Safiri","year":"2022","journal-title":"Eur. J. Prev. Cardiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.jcmg.2016.07.005","article-title":"Noninvasive CT-derived FFR based on structural and fluid analysis: A comparison with invasive FFR for detection of functionally significant stenosis","volume":"10","author":"Ko","year":"2017","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"10076","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: The success of u-net","volume":"46","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_5","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla N., Y., and Kainz, B. (2018). Attention u-net: Learning where to look for the pancreas. arXiv."},{"key":"ref_6","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., and Xu, Y. (2024). A Survey on Transformer Compression. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42826","DOI":"10.1109\/ACCESS.2019.2908039","article-title":"Coronary arteries segmentation based on 3D FCN with attention gate and level set function","volume":"7","author":"Shen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pan, L.-S., Li, C.W., Su, S.F., Tay, S.Y., Tran, Q.V., and Chan, W.P. (2021). Coronary artery segmentation under class imbalance using a U-Net based architecture on computed tomography angiography images. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-93889-z"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1002\/mp.14810","article-title":"Automatic delineation of cardiac substructures using a region-based fully convolutional network","volume":"48","author":"Harms","year":"2021","journal-title":"Med. Phys."},{"key":"ref_10","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Adv. Neural Inf. Process. Syst., 28."},{"key":"ref_11","first-page":"245","article-title":"A coronary artery CTA segmentation approach based on deep learning","volume":"30","author":"Huang","year":"2022","journal-title":"J. X-Ray Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6945","DOI":"10.1002\/mp.15842","article-title":"A novel end-to-end deep learning solution for coronary artery segmentation from CCTA","volume":"49","author":"Dong","year":"2022","journal-title":"Med. Phys."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, W., Shen, W., Li, N., Chen, Y.J., Li, S., Chen, B., Guo, S.J., and Wang, Y.Q. (2021). Automatic segmentation of the cardiac MR images based on nested fully convolutional dense network with dilated convolution. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102684"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, S., Tian, Y., Qi, Z., Wu, Y., Gao, W.Z., and Wu, Y.H. (2023). Two-stage training strategy combined with neural network for segmentation of internal mammary artery graft. Biomed. Signal Process. Control, 80.","DOI":"10.1016\/j.bspc.2022.104278"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102745","DOI":"10.1016\/j.media.2023.102745","article-title":"A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation","volume":"85","author":"Dong","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zeng, A., Wu, C., Huang, M., Zhuang, J., Bi, S., Pan, D., Ullah, N., Khan, K.N., Wang, T., and Shi, Y. (2022). ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images. arXiv.","DOI":"10.1016\/j.compmedimag.2023.102287"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023). Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation. arXiv.","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"ref_18","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","unstructured":"Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., and Babenko, A. (2021). Label-efficient semantic segmentation with diffusion models. arXiv."},{"key":"ref_20","unstructured":"Amit, T., Shaharbany, T., Nachmani, E., and Wolf, L. (2021). Segdiff: Image segmentation with diffusion probabilistic models. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rahman, A., Valanarasu, J.M.J., Hacihaliloglu, I., and Patel, V.M. (2023, January 17\u201324). Ambiguous medical image segmentation using diffusion models. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01110"},{"key":"ref_22","unstructured":"Wu, J., Fu, R., Fang, H., Zhang, Y., Yang, Y., Xiong, H., Liu, H., and Xu, Y. (2022). Medsegdiff: Medical image segmentation with diffusion probabilistic model. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zou, J., Zhu, Z., Ye, Y., and Wang, X. (2023). DiffBEV: Conditional Diffusion Model for Bird\u2019s Eye View Perception. arXiv.","DOI":"10.1609\/aaai.v38i7.28620"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Feng, C.M. (2024, January 6\u201310). Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Marrakech, Morocco.","DOI":"10.1007\/978-3-031-72111-3_24"},{"key":"ref_25","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lin, G., Liu, F., Yao, R., and Shen, C. (2019, January 15\u201320). Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00536"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H., and Xu, D. (2022, January 3\u20138). Unetr: Transformers for 3d medical image segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., and Zhang, Z. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_30","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., and Liang, J. (2018, January 20). Unet++: A nested u-net architecture for medical image segmentation. Proceedings of the Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain. Proceedings 4."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TMI.2022.3211764","article-title":"CAT-Net: A cross-slice attention transformer model for prostate zonal segmentation in MRI","volume":"42","author":"Hung","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","unstructured":"Wang, H., Cao, P., Wang, J., and Zaiane, O.R. (March, January 22). Uctransnet: Rethinking the skip connections in u-net from a channel-wise perspective with transformer. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"109887","DOI":"10.1016\/j.engappai.2024.109887","article-title":"An Efficient Large Kernel Convolution Network Designed for Neural Processing Unit","volume":"142","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022, January 18\u201324). A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Cai, D., Dong, Z., Xiao, Y., Shi, Y., and Liu, K. (2022, January 26\u201328). CNXA: A Novel Attention Mechanism Aided Convolution Network. Proceedings of the 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS), Chengdu, China.","DOI":"10.1109\/CCIS57298.2022.10016388"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1333","DOI":"10.3934\/ipi.2020057","article-title":"Automatic segmentation of the femur and tibia bones from X-ray images based on pure dilated residual U-Net","volume":"15","author":"Shen","year":"2020","journal-title":"Inverse Probl. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Han, J., and Ding, G. (2022, January 18\u201324). Scaling up your kernels to 31\u00d731: Revisiting large kernel design in cnns. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01166"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, Y., Liu, J., Zhang, X., Qi, X., and Jia, J. (2023, January 17\u201324). LargeKernel3D: Scaling Up Kernels in 3D Sparse CNNs. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01296"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, H., Nan, Y., and Yang, G. (2022, January 27\u201329). LKAU-Net: 3D large-kernel attention-based u-net for automatic MRI brain tumor segmentation. Proceedings of the Annual Conference on Medical Image Understanding and Analysis, Cambridge, UK.","DOI":"10.1007\/978-3-031-12053-4_24"},{"key":"ref_41","first-page":"1140","article-title":"Segnext: Rethinking convolutional attention design for semantic segmentation","volume":"35","author":"Guo","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","unstructured":"Liu, S.W., Chen, T.L., Chen, X.H., Chen, X.X., Xiao, Q., Wu, B.Q., K\u00e4rkk\u00e4inen, T., Pechenizkiy, M., Mocanu, D., and Wang, Z.Y. (2022). More convnets in the 2020s: Scaling up kernels beyond 51\u00d751 using sparsity. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jafari, M., Auer, D., Francis, S., Garibaldi, J., and Chen, X. (2020, January 3\u20137). DRU-Net: An efficient deep convolutional neural network for medical image segmentation. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Iowa City, IA, USA.","DOI":"10.1109\/ISBI45749.2020.9098391"},{"key":"ref_44","unstructured":"Ulhaq, A., Akhtar, N., and Pogrebna, G. (2022). Efficient Diffusion Models for Vision: A Survey. arXiv."},{"key":"ref_45","unstructured":"Cao, H.Q., Tan, C., Gao, Z.Y., Xu, Y.L., Chen, G.Y., Heng, P.-A., and Li, S.Z. (2022). A Survey on Generative Diffusion Model. arXiv."},{"key":"ref_46","unstructured":"Bieder, F., Wolleb, J., Durrer, A., Sandk\u00fchler, R., and Cattin, P. (2023, January 10\u201312). Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing. Proceedings of the Medical Imaging with Deep Learning, Nashville, TN, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mehta, D., Skliar, A., Ben Yahia, H., Borse, S., Porikli, F., Habibian, A., and Blankevoort, T. (2022, January 18\u201324). Simple and Efficient Architectures for Semantic Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00296"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, Q., Li, B., Xiao, T., Zhu, J., Li, C., Wong, D.F., and Chao, L.S. (2019). Learning deep transformer models for machine translation. arXiv.","DOI":"10.18653\/v1\/P19-1176"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Wang, Q.L., Wu, B.G., Zhu, P.F., Li, P.H., Zuo, W.M., and Hu, Q.H. (2020, January 14\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"102999","DOI":"10.1016\/j.media.2023.102999","article-title":"AVDNet: Joint coronary artery and vein segmentation with topological consistency","volume":"91","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2980","DOI":"10.1016\/j.jacc.2020.11.021","article-title":"The global burden of cardiovascular diseases and risks: A compass for global action","volume":"76","author":"Roth","year":"2020","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_55","unstructured":"Lee, H.H., Bao, S.X., Huo, Y.K., and Landman, B.A. (2022). 3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Yu, L.Q., Cheng, J.Z., Dou, Q., Yang, X., Chen, H., Qin, J., and Heng, P.A. (2017, January 11\u201313). Automatic 3D cardiovascular MR segmentation with densely-connected volumetric convnets. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2017: 20th International Conference, Quebec City, QC, Canada. Proceedings, Part II 20.","DOI":"10.1007\/978-3-319-66185-8_33"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.Y. (2023). Segment anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/6\/192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:49:50Z","timestamp":1760032190000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/6\/192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,11]]},"references-count":57,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["jimaging11060192"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11060192","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,11]]}}}