{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T19:50:39Z","timestamp":1781898639616,"version":"3.54.5"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T00:00:00Z","timestamp":1776643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Parallel Prog"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10766-026-00826-6","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:56:14Z","timestamp":1776711374000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Implementation of AI Algorithms on an FPGA-Based System for Enhancing Blood Vessel Segmentation"],"prefix":"10.1007","volume":"54","author":[{"given":"Majed","family":"Alsharari","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Son T.","family":"Mai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Romain","family":"Garnier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Rea\u00f1o","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roger","family":"Woods","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"826_CR1","doi-asserted-by":"publisher","first-page":"38202","DOI":"10.1109\/ACCESS.2022.3163247","volume":"10","author":"OO Sule","year":"2022","unstructured":"Sule, O.O.: A survey of deep learning for retinal blood vessel segmentation methods: taxonomy, trends, challenges and future directions. IEEE Access 10, 38202\u201338236 (2022)","journal-title":"IEEE Access"},{"key":"826_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2018.06.007","volume":"46","author":"A Carballal","year":"2018","unstructured":"Carballal, A., Novoa, F.J., Fernandez-Lozano, C., et al.: Automatic multiscale vascular image segmentation algorithm for coronary angiography. Biomed. Signal Process. Control 46, 1\u20139 (2018)","journal-title":"Biomed. Signal Process. Control"},{"issue":"11","key":"826_CR3","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2016","unstructured":"Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369\u20132380 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"9","key":"826_CR4","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1109\/JBHI.2022.3188710","volume":"26","author":"W Liu","year":"2022","unstructured":"Liu, W., et al.: Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation. IEEE J. Biomed. Health Inform. 26(9), 4623\u20134634 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"826_CR5","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation, arXiv preprint https:\/\/arxiv.org\/1411.4038 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"826_CR6","doi-asserted-by":"publisher","unstructured":"Kamran, S.A., et\u00a0al.: RV-GAN: segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12908. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-87237-3_4 (2021)","DOI":"10.1007\/978-3-030-87237-3_4"},{"key":"826_CR7","doi-asserted-by":"crossref","unstructured":"Guo, C., Szemenyei, M., Yi, Y., et\u00a0al.: Sa-unet: spatial attention u-net for retinal vessel segmentation arXiv preprint https:\/\/arxiv.org\/2004.03696 (2021)","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"826_CR8","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","volume":"178","author":"Q Jin","year":"2019","unstructured":"Jin, Q., Meng, Z., Pham, T.D., et al.: Dunet: a deformable network for retinal vessel segmentation. Knowl.-Based Syst. 178, 149\u2013162 (2019)","journal-title":"Knowl.-Based Syst."},{"key":"826_CR9","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., et\u00a0al.: Attention u-net: learning where to look for the pancreas. arXiv preprint http:\/\/arxiv.org\/abs\/1804.03999 (2018)"},{"key":"826_CR10","unstructured":"Zhou, Z., et\u00a0al.: Unet++: a nested u-net architecture for medical image segmentation arXiv preprint http:\/\/arxiv.org\/1807.10165 (2018)"},{"key":"826_CR11","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: x U-net: convolutional networks for biomedical image segmentation In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science(), vol 9351. Springer, Cham.  https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 (1999)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"11","key":"826_CR12","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"826_CR13","unstructured":"He, K., Zhang, X., Ren, S., et\u00a0al.: Deep residual learning for image recognition arXiv preprint arXiv:1512.03385 https:\/\/arxiv.org\/abs\/1512.03385 (2016)"},{"key":"826_CR14","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint http:\/\/arxiv.org\/abs\/1409.0473 (2014)"},{"key":"826_CR15","doi-asserted-by":"publisher","unstructured":"DeWolf, T., Jaworski, P., Eliasmith, C.: Nengo and low-power AI hardware for robust, embedded neurorobotics. Front. Neurorobot. 14, 568359 https:\/\/doi.org\/10.3389\/fnbot.2020.568359 (2020)","DOI":"10.3389\/fnbot.2020.568359"},{"key":"826_CR16","doi-asserted-by":"publisher","unstructured":"Alsharari, M., et\u00a0al.: Multi-spectral in-vivo FPGA-based surgical imaging In: Gan, L., Wang, Y., Xue, W., Chau, T. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. Lecture Notes in Computer Science, vol 13569. Springer https:\/\/doi.org\/10.1007\/978-3-031-19983-7_8 (2022)","DOI":"10.1007\/978-3-031-19983-7_8"},{"key":"826_CR17","doi-asserted-by":"crossref","unstructured":"Alsharari, M., et\u00a0al.: An intelligent image processing system for enhancing blood vessel segmentation on low-power SoC IEEE International Symposium on Systems, Architectures, Modeling, and Simulation, Samos, Greece, pp123-138, (2023)","DOI":"10.1007\/978-3-031-46077-7_9"},{"issue":"1","key":"826_CR18","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.cmpb.2012.03.009","volume":"108","author":"MM Fraz","year":"2012","unstructured":"Fraz, M.M., Remagnino, P., Hoppe, A., et al.: Blood vessel segmentation methodologies in retinal images-a survey. Comput. Methods Programs Biomed. 108(1), 407\u2013433 (2012)","journal-title":"Comput. Methods Programs Biomed."},{"key":"826_CR19","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.cmpb.2018.02.001","volume":"158","author":"S Moccia","year":"2018","unstructured":"Moccia, S., De Momi, E., El Hadji, S., et al.: Blood vessel segmentation algorithms\u2014review of methods, datasets and evaluation metrics. Comput. Methods Programs Biomed. 158, 71\u201391 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"826_CR20","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/42.34715","volume":"8","author":"S Chaudhuri","year":"1989","unstructured":"Chaudhuri, S., et al.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263\u2013269 (1989)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"826_CR21","doi-asserted-by":"publisher","unstructured":"Mart\u00ednez P\u00e9rez, M.E., Hughes, A.D., Stanton, A.V., et\u00a0al.: Retinal blood vessel segmentation by means of scale-space analysis and region growing Medical Image Computing and Computer-Assisted Intervention, Lecture Notes in Computer Science, vol 1679. Springer, https:\/\/doi.org\/10.1007\/10704282_10 (1999)","DOI":"10.1007\/10704282_10"},{"key":"826_CR22","doi-asserted-by":"publisher","unstructured":"Frangi, A.F., Niessen, W.J., Vincken, K.L., et\u00a0al.: Multiscale vessel enhancement filtering Medical Image Computing and Computer-Assisted Intervention. Lecture Notes in Computer Science, vol 1496. Springer. https:\/\/doi.org\/10.1007\/BFb0056195 (1998)","DOI":"10.1007\/BFb0056195"},{"issue":"6","key":"826_CR23","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.3233\/BME-141149","volume":"24","author":"J Yang","year":"2014","unstructured":"Yang, J., et al.: Improved hessian multiscale enhancement filter. Bio-Med. Mater. Eng. 24(6), 3267\u20133275 (2014)","journal-title":"Bio-Med. Mater. Eng."},{"key":"826_CR24","doi-asserted-by":"publisher","unstructured":"Elbalaoui, A., Fakir, M., Taifi, K., et\u00a0al.: Automatic detection of blood vessel in retinal images. 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV),  https:\/\/doi.org\/10.1109\/CGiV.2016.69 (2016)","DOI":"10.1109\/CGiV.2016.69"},{"key":"826_CR25","doi-asserted-by":"publisher","unstructured":"Pizer, S., Johnston, R., Ericksen, J., et\u00a0al.: Contrast-limited adaptive histogram equalization: speed and effectiveness. First Conference on Visualization in Biomedical Computing, USA, pp. 337-345, https:\/\/doi.org\/10.1109\/VBC.1990.109340 (1999)","DOI":"10.1109\/VBC.1990.109340"},{"key":"826_CR26","doi-asserted-by":"publisher","unstructured":"Niemeijer, M., et\u00a0al.: Comparative study of retinal vessel segmentation methods on a new publicly available database. Proceedings Volume 5370, Medical Imaging 2004: Image Processing; https:\/\/doi.org\/10.1117\/12.535349 (2004)","DOI":"10.1117\/12.535349"},{"issue":"1","key":"826_CR27","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119\u2013139 (1997)","journal-title":"J. Comput. Syst. Sci."},{"issue":"5","key":"826_CR28","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TITB.2010.2052282","volume":"14","author":"CA Lupascu","year":"2010","unstructured":"Lupascu, C.A., Tegolo, D., Trucco, E.: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans. Inf Technol. Biomed. 14(5), 1267\u20131274 (2010)","journal-title":"IEEE Trans. Inf Technol. Biomed."},{"issue":"12","key":"826_CR29","doi-asserted-by":"publisher","first-page":"e0188939","DOI":"10.1371\/journal.pone.0188939","volume":"12","author":"N Memari","year":"2017","unstructured":"Memari, N., et al.: Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier. PLoS ONE 12(12), e0188939 (2017)","journal-title":"PLoS ONE"},{"issue":"1","key":"826_CR30","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1109\/72.363449","volume":"6","author":"R Nekovei","year":"1995","unstructured":"Nekovei, R., Sun, Y.: Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans. Neural Netw. 6(1), 64\u201372 (1995)","journal-title":"IEEE Trans. Neural Netw."},{"key":"826_CR31","doi-asserted-by":"publisher","unstructured":"Nandy, M., Banerjee, M.: Retinal vessel segmentation using Gabor filter and artificial neural network. International Conference on Emerging Applications of Information Technology, India, pp. 157-160, https:\/\/doi.org\/10.1109\/EAIT.2012.6407885 (2012)","DOI":"10.1109\/EAIT.2012.6407885"},{"key":"826_CR32","doi-asserted-by":"publisher","first-page":"103053","DOI":"10.1016\/j.bspc.2021.103053","volume":"70","author":"B Topta\u015f","year":"2021","unstructured":"Topta\u015f, B., Hanbay, D.: Retinal blood vessel segmentation using pixel-based feature vector. Biomed. Signal Process. Control 70, 103053 (2021)","journal-title":"Biomed. Signal Process. Control"},{"issue":"4","key":"826_CR33","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/JBHI.2018.2872813","volume":"23","author":"Z Yan","year":"2018","unstructured":"Yan, Z., Yang, X., Cheng, K.T.: A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J. Biomed. Health Inform. 23(4), 1427\u20131436 (2018)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"2","key":"826_CR34","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s11063-019-10011-1","volume":"52","author":"Q Jin","year":"2020","unstructured":"Jin, Q., Chen, Q., Meng, Z., et al.: Construction of retinal vessel segmentation models based on convolutional neural network. Neural Process. Lett. 52(2), 1005\u20131022 (2020)","journal-title":"Neural Process. Lett."},{"issue":"4","key":"826_CR35","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"24","key":"826_CR36","doi-asserted-by":"publisher","first-page":"5507","DOI":"10.3390\/app9245507","volume":"9","author":"F Cervantes-Sanchez","year":"2019","unstructured":"Cervantes-Sanchez, F., Cruz-Aceves, I., Hernandez-Aguirre, A., et al.: Automatic segmentation of coronary arteries in x-ray angiograms using multiscale analysis and artificial neural networks. Appl. Sci. 9(24), 5507 (2019)","journal-title":"Appl. Sci."},{"key":"826_CR37","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system, arXiv reprint http:\/\/arxiv.org\/abs\/1603.02754 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"826_CR38","unstructured":"Ke, G., Meng, Q., Finley, T., et\u00a0al.: Lightgbm: a highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017)"},{"key":"826_CR39","unstructured":"Dorogush, A.V., Ershov, V., Gulin, A.: Catboost: gradient boosting with categorical features support. arXiv preprint http:\/\/arxiv.org\/abs\/1810.11363 (2018)"},{"key":"826_CR40","unstructured":"O\u2019Malley, T., Bursztein, E., Long, J., et\u00a0al.: Kerastuner. https:\/\/github.com\/keras-team\/keras-tuner (2019)"},{"key":"826_CR41","unstructured":"Coelho, C.N., Kuusela, A., Li, S., et\u00a0al.: Automatic deep heterogeneous quantization of deep neural networks for ultra low-area, low-latency inference on the edge at particle colliders. arXiv preprint http:\/\/arxiv.org\/abs\/2006.10159 6 (2020)"},{"key":"826_CR42","unstructured":"Abadi, M., Agarwal, A., Barham, P., et\u00a0al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint http:\/\/arxiv.org\/abs\/1603.04467 (2016)"},{"key":"826_CR43","unstructured":"Conifer: Fast inference of Boosted Decision Trees in FPGAs. https:\/\/github.com\/thesps\/conifer (2021)"},{"key":"826_CR44","doi-asserted-by":"publisher","first-page":"P05026","DOI":"10.1088\/1748-0221\/15\/05\/P05026","volume":"15","author":"S Summers","year":"2020","unstructured":"Summers, S., et al.: Fast inference of boosted decision trees in FPGAs for particle physics. J. Instrum. 15, P05026 (2020)","journal-title":"J. Instrum."},{"key":"826_CR45","doi-asserted-by":"crossref","unstructured":"Duarte, J., et\u00a0al.: Fast inference of deep neural networks in FPGAs for particle physics. JINST 13(07):P07027. (2018) http:\/\/arxiv.org\/abs\/1804.06913 [physics.ins-det]","DOI":"10.1088\/1748-0221\/13\/07\/P07027"},{"issue":"3","key":"826_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3242900","volume":"11","author":"S Liu","year":"2018","unstructured":"Liu, S., et al.: Optimizing CNN-based segmentation with deeply customized convolutional and deconvolutional architectures on FPGA. ACM Trans. Reconfig. Technol. Syst. (TRETS) 11(3), 1\u201322 (2018)","journal-title":"ACM Trans. Reconfig. Technol. Syst. (TRETS)"},{"key":"826_CR47","doi-asserted-by":"publisher","unstructured":"Miyama, M.: FPGA implementation of 3-bit quantized CNN for semantic segmentation. J. Phys.: Conf. Ser. 1729 012004 (2021) https:\/\/doi.org\/10.1088\/1742-6596\/1729\/1\/012004","DOI":"10.1088\/1742-6596\/1729\/1\/012004"},{"key":"826_CR48","doi-asserted-by":"crossref","unstructured":"Papatheofanous, E., Tziolos, P., Kalekis, V., et\u00a0al.: Soc fpga acceleration for semantic segmentation of clouds in satellite images (2022)","DOI":"10.1109\/VLSI-SoC54400.2022.9939585"}],"container-title":["International Journal of Parallel Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10766-026-00826-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10766-026-00826-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10766-026-00826-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T18:53:30Z","timestamp":1781895210000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10766-026-00826-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":48,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["826"],"URL":"https:\/\/doi.org\/10.1007\/s10766-026-00826-6","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4351485\/v1","asserted-by":"object"}]},"ISSN":["0885-7458","1573-7640"],"issn-type":[{"value":"0885-7458","type":"print"},{"value":"1573-7640","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"30 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"8"}}