{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:15:55Z","timestamp":1790626555986,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T00:00:00Z","timestamp":1690761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qatar University student grant","award":["QUST-1-CENG-2023-796"],"award-info":[{"award-number":["QUST-1-CENG-2023-796"]}]},{"name":"Qatar University student grant","award":["PSAU\/2023\/R\/1444"],"award-info":[{"award-number":["PSAU\/2023\/R\/1444"]}]},{"DOI":"10.13039\/100009392","name":"Prince Sattam Bin Abdulaziz University","doi-asserted-by":"publisher","award":["QUST-1-CENG-2023-796"],"award-info":[{"award-number":["QUST-1-CENG-2023-796"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009392","name":"Prince Sattam Bin Abdulaziz University","doi-asserted-by":"publisher","award":["PSAU\/2023\/R\/1444"],"award-info":[{"award-number":["PSAU\/2023\/R\/1444"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Intracranial hemorrhage (ICH) occurs when blood leaks inside the skull as a result of trauma to the skull or due to medical conditions. ICH usually requires immediate medical and surgical attention because the disease has a high mortality rate, long-term disability potential, and other potentially life-threatening complications. There are a wide range of severity levels, sizes, and morphologies of ICHs, making accurate identification challenging. Hemorrhages that are small are more likely to be missed, particularly in healthcare systems that experience high turnover when it comes to computed tomography (CT) investigations. Although many neuroimaging modalities have been developed, CT remains the standard for diagnosing trauma and hemorrhage (including non-traumatic ones). A CT scan-based diagnosis can provide time-critical, urgent ICH surgery that could save lives because CT scan-based diagnoses can be obtained rapidly. The purpose of this study is to develop a machine-learning algorithm that can detect intracranial hemorrhage based on plain CT images taken from 75 patients. CT images were preprocessed using brain windowing, skull-stripping, and image inversion techniques. Hemorrhage segmentation was performed using multiple pre-trained models on preprocessed CT images. A U-Net model with DenseNet201 pre-trained encoder outperformed other U-Net, U-Net++, and FPN (Feature Pyramid Network) models with the highest Dice similarity coefficient (DSC) and intersection over union (IoU) scores, which were previously used in many other medical applications. We presented a three-dimensional brain model highlighting hemorrhages from ground truth and predicted masks. The volume of hemorrhage was measured volumetrically to determine the size of the hematoma. This study is essential in examining ICH for diagnostic purposes in clinical practice by comparing the predicted 3D model with the ground truth.<\/jats:p>","DOI":"10.3390\/diagnostics13152537","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:39:32Z","timestamp":1690767572000},"page":"2537","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Deep Learning-Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection Using Computed Tomography Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8207-0227","authenticated-orcid":false,"given":"Muntakim Mahmud","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0744-8206","authenticated-orcid":false,"given":"Muhammad E. H.","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. S. M. Shamsul","family":"Arefin","sequence":"additional","affiliation":[{"name":"Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanchon Kanti","family":"Podder","sequence":"additional","affiliation":[{"name":"Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. Sakib Abrar","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Biomedical Physics and Technology, University of Dhaka, Dhaka 1000, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulrahman","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Medical Equipment Technology, College of Applied, Medical Science, Majmaah University, Majmaah City 11952, Saudi Arabia"},{"name":"Department of Biomedical Technology, College of Applied Medical Sciences in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5839-4589","authenticated-orcid":false,"given":"M.","family":"Murugappan","sequence":"additional","affiliation":[{"name":"Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, Doha 13133, Kuwait"},{"name":"Department of Electronics and Communication Engineering, School of Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai 600117, India"},{"name":"Center of Excellence for Unmanned Aerial Systems (CoEUAS), Universiti Malaysia Perlis, Perlis 02600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7068-9112","authenticated-orcid":false,"given":"Amith","family":"Khandakar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Mushtak","sequence":"additional","affiliation":[{"name":"Clinical Imaging Department, Hamad Medical Corporation, Doha 3050, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md.","family":"Nahiduzzaman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qatar University, Doha 2713, Qatar"},{"name":"Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pak, M., and Kim, S. (2017, January 8\u201310). A review of deep learning in image recognition. Proceedings of the 2017 IEEE 4th International Conference on Computer Applications and Information Processing Technology (CAIPT), Kuta Bali, Indonesia.","DOI":"10.1109\/CAIPT.2017.8320684"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/JBHI.2020.3028243","article-title":"Deep learning for hemorrhagic lesion detection and segmentation on brain CT images","volume":"25","author":"Li","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1007\/s12559-021-09955-1","article-title":"Deep learning for reliable classification of COVID-19, MERS, and SARS from chest X-ray images","volume":"14","author":"Tahir","year":"2022","journal-title":"Cogn. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Degerli, A., Kiranyaz, S., Chowdhury, M.E., and Gabbouj, M. (2022, January 16\u201319). Osegnet: Operational segmentation network for Covid-19 detection using chest X-ray images. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France.","DOI":"10.1109\/ICIP46576.2022.9897412"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105620","DOI":"10.1016\/j.compbiomed.2022.105620","article-title":"Deep learning techniques for liver and liver tumor segmentation: A review","volume":"147","author":"Gul","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Qiblawey, Y., Tahir, A., Chowdhury, M.E., Khandakar, A., Kiranyaz, S., Rahman, T., Ibtehaz, N., Mahmud, S., Maadeed, S.A., and Musharavati, F. (2021). Detection and severity classification of COVID-19 in CT images using deep learning. Diagnostics, 11.","DOI":"10.3390\/diagnostics11050893"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1007\/s003300000800","article-title":"Intracranial hemorrhage: Principles of CT and MRI interpretation","volume":"11","author":"Parizel","year":"2001","journal-title":"Eur. Radiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1053\/j.sult.2022.02.004","article-title":"Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging","volume":"Volume 43","author":"Zhu","year":"2022","journal-title":"Seminars in Ultrasound, CT and MRI"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Chang, C.S., Yan, J.L., Ko, L., and Chang, T.S. (2019, January 18\u201320). Semantic segmentation of intracranial hemorrhages in head CT scans. Proceedings of the 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China.","DOI":"10.1109\/ICSESS47205.2019.9040733"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"102054","DOI":"10.1016\/j.media.2021.102054","article-title":"CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification","volume":"71","author":"Goncharov","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kwon, D., Ahn, J., Kim, J., Choi, I., Jeong, S., Lee, Y.-S., Park, J., and Lee, M. (2019, January 13\u201317). Siamese U-Net with healthy template for accurate segmentation of intracranial hemorrhage. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China.","DOI":"10.1007\/978-3-030-32248-9_94"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00779-020-01492-2","article-title":"Synergic deep learning model\u2013based automated detection and classification of brain intracranial hemorrhage images in wearable networks","volume":"26","author":"Anupama","year":"2022","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.18280\/ts.380616","article-title":"Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model","volume":"38","author":"Vamsi","year":"2021","journal-title":"Trait. Du Signal"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, J.L., Farooq, H., Zhuang, H., and Ibrahim, A.K. (2020). Segmentation of intracranial hemorrhage using semi-supervised multi-task attention-based U-net. Appl. Sci., 10.","DOI":"10.3390\/app10093297"},{"key":"ref_15","unstructured":"Gautam, A., and Raman, B. (2019). Machine Intelligence and Signal Analysis, Springer."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.irbm.2022.05.002","article-title":"A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods, Datasets, Libraries, and Future Directions","volume":"43","author":"Balwant","year":"2022","journal-title":"IRBM"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rehman, M.U., Cho, S., Kim, J., and Chong, K.T. (2021). Brainseg-net: Brain tumor mr image segmentation via enhanced encoder\u2013decoder network. Diagnostics, 11.","DOI":"10.3390\/diagnostics11020169"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106426","DOI":"10.1016\/j.compbiomed.2022.106426","article-title":"RAAGR2-Net: A brain tumor segmentation network using parallel processing of multiple spatial frames","volume":"152","author":"Rehman","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103541","DOI":"10.1016\/j.bspc.2022.103541","article-title":"MR brain segmentation based on DE-ResUnet combining texture features and background knowledge","volume":"75","author":"Wu","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"448","DOI":"10.3174\/ajnr.A7419","article-title":"Automated 3D fetal brain segmentation using an optimized deep learning approach","volume":"43","author":"Zhao","year":"2022","journal-title":"Am. J. Neuroradiol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Salman Khan, M., Ullah, A., Khan, K.N., Riaz, H., Yousafzai, Y.M., Rahman, T., Chowdhury, M.E., and Abul Kashem, S.B. (2022). Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102405"},{"key":"ref_22","first-page":"14","article-title":"Computed tomography images for intracranial hemorrhage detection and segmentation","volume":"5","author":"Hssayeni","year":"2020","journal-title":"Intracranial Hemorrhage Segm. Using A Deep. Convolutional Model Data"},{"key":"ref_23","unstructured":"Murphy, A., and Baba, Y. (2023, May 17). Windowing (CT). Reference Article, Radiopaedia. org. ed. Available online: https:\/\/radiopaedia.org\/articles\/windowing-ct?lang=us."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1152\/japplphysiol.00986.2005","article-title":"Protocol for measurement of liver fat by computed tomography","volume":"100","author":"Davidson","year":"2006","journal-title":"J. Appl. Physiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2019.04.030","article-title":"Automated brain extraction from head CT and CTA images using convex optimization with shape propagation","volume":"176","author":"Najm","year":"2019","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_26","first-page":"1213","article-title":"An efficient implementation of artificial neural networks with K-fold cross-validation for process optimization","volume":"20","author":"Srinivasan","year":"2019","journal-title":"J. Internet Technol."},{"key":"ref_27","first-page":"1","article-title":"Classification of viral, bacterial, and COVID-19 pneumonia using deep learning framework from chest X-ray images","volume":"12","author":"Chowdhury","year":"2022","journal-title":"Comput. Sci. Inf. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Khandakar, A., Chowdhury, M.E.H., Reaz, M.B.I., Ali, S.H.M., Kiranyaz, S., Rahman, T., Chowdhury, M.H., Ayari, M.A., Alfkey, R., and Bakar, A.A.A. (2022). A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images. Sensors, 22.","DOI":"10.3390\/s22114249"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"53359","DOI":"10.1109\/ACCESS.2022.3173424","article-title":"HipXNet: Deep Learning Approaches to Detect Aseptic Loos-Ening of Hip Implants Using X-ray Images","volume":"10","author":"Rahman","year":"2022","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"44247","DOI":"10.1109\/ACCESS.2019.2908991","article-title":"Nas-unet: Neural architecture search for medical image segmentation","volume":"7","author":"Weng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tran, S.-T., Cheng, C.-H., Nguyen, T.-T., Le, M.-H., and Liu, D.-G. (2021). TMD-Unet: Triple-Unet with multi-scale input features and dense skip connection for medical image segmentation. Healthcare, 9.","DOI":"10.3390\/healthcare9010054"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9854084","DOI":"10.34133\/2022\/9854084","article-title":"Jointly optimized spatial histogram UNET architecture (JOSHUA) for adipose tissue segmentation","volume":"2022","author":"Peeples","year":"2022","journal-title":"BME Front."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17461","DOI":"10.1007\/s00521-023-08606-w","article-title":"BIO-CXRNET: A robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data","volume":"35","author":"Rahman","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1109\/TCSVT.2015.2477937","article-title":"Multi-loss regularized deep neural network","volume":"26","author":"Xu","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105002","DOI":"10.1016\/j.compbiomed.2021.105002","article-title":"COVID-19 infection localization and severity grading from chest X-ray images","volume":"139","author":"Tahir","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_39","unstructured":"Yi-de, M., Qing, L., and Zhi-Bai, Q. (2004, January 20\u201322). Automated image segmentation using improved PCNN model based on cross-entropy. Proceedings of the 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, China."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jadon, S. (2020, January 27\u201329). A survey of loss functions for semantic segmentation. Proceedings of the 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Vina del Mar, Chile.","DOI":"10.1109\/CIBCB48159.2020.9277638"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.proeng.2012.01.952","article-title":"3D reconstruction of face from 2D CT scan images","volume":"30","author":"Kumar","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_42","first-page":"36","article-title":"Image processing with ImageJ","volume":"11","author":"Ram","year":"2004","journal-title":"Biophotonics Int."},{"key":"ref_43","first-page":"270","article-title":"Effect of windowing and zero-filled reconstruction of MRI data on spatial resolution and acquisition strategy","volume":"14","author":"Bernstein","year":"2001","journal-title":"J. Magn. Reson. Imaging Off. J. Int. Soc. Magn. Reson. Med."},{"key":"ref_44","first-page":"619","article-title":"Review of CT image reconstruction open source toolkits","volume":"28","author":"Shi","year":"2020","journal-title":"J. X-ray Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1109\/TMI.2002.804441","article-title":"A review of cardiac image registration methods","volume":"21","author":"Makela","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.mibio.2004.04.006","article-title":"Positron emission tomography\/computed tomography\u2013imaging protocols, artifacts, and pitfalls","volume":"6","author":"Bockisch","year":"2004","journal-title":"Mol. Imaging Biol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11760-012-0298-0","article-title":"Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging","volume":"8","author":"Bhadauria","year":"2014","journal-title":"Signal Image Video Process."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/15\/2537\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:22:48Z","timestamp":1760127768000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/13\/15\/2537"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,31]]},"references-count":47,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["diagnostics13152537"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics13152537","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,31]]}}}