{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T18:00:58Z","timestamp":1793728858263,"version":"4.6.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T00:00:00Z","timestamp":1747785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-98427-9","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T12:39:25Z","timestamp":1747831165000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17"],"prefix":"10.1038","volume":"15","author":[{"given":"Rahul","family":"Singh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheifali","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmad","family":"Almogren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ateeq Ur","family":"Rehman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salil","family":"Bharany","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayman","family":"Altameem","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeyoung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,21]]},"reference":[{"key":"98427_CR1","doi-asserted-by":"crossref","unstructured":"Ansari, M. Y. et al. Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Med. Imaging 22(1),  97 (2022).","DOI":"10.1186\/s12880-022-00869-4"},{"key":"98427_CR2","doi-asserted-by":"crossref","unstructured":"Almotairi, S., Kareem, G., Aouf, M., Almutairi, B. & Salem, M. A. M. Liver tumor segmentation in CT scans using modified SegNet. Sensors 20(5),  1516 (2020).","DOI":"10.3390\/s20051516"},{"key":"98427_CR3","doi-asserted-by":"crossref","unstructured":"Marino, D., Zichi, C., Audisio, M., Sperti, E. & Di Maio, M. Second-line treatment options in hepatocellular carcinoma. Drugs in Context  8(2019).","DOI":"10.7573\/dic.212577"},{"issue":"3","key":"98427_CR4","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin. 71(3), 209\u2013249 (2021).","journal-title":"Cancer J. Clin."},{"issue":"6","key":"98427_CR5","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Cancer J. Clin. 68(6), 394\u2013424 (2018).","journal-title":"Cancer J. Clin."},{"key":"98427_CR6","doi-asserted-by":"crossref","unstructured":"Lupsor-Platon, M. et al. Performance of ultrasound techniques and the potential of artificial intelligence in the evaluation of hepatocellular carcinoma and non-alcoholic fatty liver disease. Cancers  13(4),  790  (2021).","DOI":"10.3390\/cancers13040790"},{"key":"98427_CR7","doi-asserted-by":"crossref","unstructured":"Barsouk, A., Thandra, K. C., Saginala, K., Rawla, P. & Barsouk, A. Chemical risk factors of primary liver cancer: An update. Hepatic Medicine: Evid. Res. 179\u2013188 (2021).","DOI":"10.2147\/HMER.S278070"},{"key":"98427_CR8","doi-asserted-by":"publisher","first-page":"680","DOI":"10.3389\/fonc.2020.00680","volume":"10","author":"SH Zhen","year":"2020","unstructured":"Zhen, S. H. et al. Deep learning for accurate diagnosis of liver tumor based on magnetic resonance imaging and clinical data. Front. Oncol. 10, 680 (2020).","journal-title":"Front. Oncol."},{"issue":"11","key":"98427_CR9","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1038\/s41575-022-00643-5","volume":"19","author":"JC Nault","year":"2022","unstructured":"Nault, J. C., Paradis, V., Ronot, M. & Zucman-Rossi, J. Benign liver tumours: Understanding molecular physiology to adapt clinical management. Nat. Rev. Gastroenterol. Hepatol. 19(11), 703\u2013716 (2022).","journal-title":"Nat. Reviews Gastroenterol. Hepatol."},{"issue":"12","key":"98427_CR10","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1038\/s41575-020-0314-8","volume":"17","author":"H Petrowsky","year":"2020","unstructured":"Petrowsky, H. et al. Modern therapeutic approaches for the treatment of malignant liver tumours. Nat. Reviews Gastroenterol. Hepatol. 17(12), 755\u2013772 (2020).","journal-title":"Nat. Reviews Gastroenterol. Hepatol."},{"key":"98427_CR11","unstructured":"Siddiqui, M. A., Siddiqui, H. H., Mishra, A. & Usmani, A. Epidemiology of hepatocellular carcinoma. Virus  18, 19 (2018)."},{"issue":"1","key":"98427_CR12","first-page":"54","volume":"57","author":"YX Gao","year":"2020","unstructured":"Gao, Y. X. et al. Progress and prospects of biomarkers in primary liver cancer. Int. J. Oncol. 57(1), 54\u201366 (2020).","journal-title":"Int. J. Oncol."},{"key":"98427_CR13","doi-asserted-by":"crossref","unstructured":"Cantisani, V. et al. Liver metastases: Contrast-enhanced ultrasound compared with computed tomography and magnetic resonance. World J. Gastroenterol. WJG 20(29),  9998 (2014).","DOI":"10.3748\/wjg.v20.i29.9998"},{"key":"98427_CR14","doi-asserted-by":"crossref","unstructured":"Girotra, M. et al. Utility of endoscopic ultrasound and endoscopy in diagnosis and management of hepatocellular carcinoma and its complications: What does endoscopic ultrasonography offer above and beyond conventional cross-sectional imaging? World J. Gastrointest. Endosc.  10(2), 56 (2018).","DOI":"10.4253\/wjge.v10.i2.56"},{"key":"98427_CR15","doi-asserted-by":"crossref","unstructured":"Su, Y. et al. Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Comput. Biol. Med. 145, 105409  (2022).","DOI":"10.1016\/j.compbiomed.2022.105409"},{"key":"98427_CR16","doi-asserted-by":"publisher","first-page":"100569","DOI":"10.1016\/j.pacs.2023.100569","volume":"34","author":"T Sun","year":"2023","unstructured":"Sun, T. et al. In vivo liver function reserve assessments in alcoholic liver disease by scalable photoacoustic imaging. Photoacoustics 34, 100569. https:\/\/doi.org\/10.1016\/j.pacs.2023.100569 (2023).","journal-title":"Photoacoustics"},{"key":"98427_CR17","doi-asserted-by":"crossref","unstructured":"Zhang, J. et al. August. Light-weight hybrid convolutional network for liver tumor segmentation. IJCAI 19, 4271\u20134277 (2019).","DOI":"10.24963\/ijcai.2019\/593"},{"key":"98427_CR18","doi-asserted-by":"crossref","unstructured":"Chincholkar, V., Srivastava, S., Pawanarkar, A. & Chaudhari, S. January. Deep learning techniques in liver segmentation: Evaluating U-Net, attention U-Net, ResNet50, and ResUNet Models. In 2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)  775\u2013779 (IEEE, 2024).","DOI":"10.1109\/Confluence60223.2024.10463507"},{"issue":"4","key":"98427_CR19","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1109\/TLA.2023.10128927","volume":"21","author":"J Amin","year":"2023","unstructured":"Amin, J., Anjum, M. A., Sharif, M., Kadry, S. & Crespo, R. G. Visual geometry group based on U-Shaped model for liver\/liver tumor segmentation. IEEE Lat. Am. Trans. 21(4), 557\u2013564 (2023).","journal-title":"IEEE Lat. Am. Trans."},{"key":"98427_CR20","unstructured":"Lifang, C. H. E. N. & Shiyong, L. U. O. Multi-Scale liver tumor segmentation algorithm by fusing Convolution and transformer. J. Comput. Eng. Appl.  60(4)  (2024)."},{"key":"98427_CR21","doi-asserted-by":"crossref","unstructured":"You, Y., Bai, Z., Zhang, Y. & Li, Z. Contour-induced parallel graph reasoning for liver tumor segmentation. Biomed. Signal Process. Control 92,  106111 (2024).","DOI":"10.1016\/j.bspc.2024.106111"},{"key":"98427_CR22","doi-asserted-by":"crossref","unstructured":"He, J., Luo, Z., Lian, S., Su, S. & Li, S. Towards accurate abdominal tumor segmentation: A 2D model with Position-Aware and Key Slice Feature Sharing. Comput. Biol. Med. 179,  108743 (2024).","DOI":"10.1016\/j.compbiomed.2024.108743"},{"key":"98427_CR23","doi-asserted-by":"crossref","unstructured":"Shui, Y. et al. A three-path network with multi-scale selective feature fusion, edge-inspiring, and edge-guiding for liver tumor segmentation. Comput. Biol. Med. 168, 107841 (2024).","DOI":"10.1016\/j.compbiomed.2023.107841"},{"issue":"5","key":"98427_CR24","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1007\/s42979-024-02991-2","volume":"5","author":"A Biswas","year":"2024","unstructured":"Biswas, A., Maity, S. P., Banik, R., Bhattacharya, P. & Debbarma, J. GAN-Driven liver tumor segmentation: enhancing accuracy in biomedical imaging. SN Comput. Sci. 5(5), 652 (2024).","journal-title":"SN Comput. Sci."},{"issue":"12","key":"98427_CR25","doi-asserted-by":"publisher","first-page":"3167","DOI":"10.1007\/s11517-023-02876-1","volume":"61","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Zhu, J., Fu, S. & Ye, Y. Context fusion network with multi-scale-aware skip connection and twin-split attention for liver tumor segmentation. Med. Biol. Eng. Comput. 61(12), 3167\u20133180 (2023).","journal-title":"Med. Biol. Eng. Comput."},{"key":"98427_CR26","doi-asserted-by":"crossref","unstructured":"Muhammad, S. & Zhang, J. Segmentation of Liver Tumors by Monai and PyTorch in CT Images with Deep Learning Techniques. Appl. Sciences 14(12),  5144 (2024).","DOI":"10.3390\/app14125144"},{"key":"98427_CR27","unstructured":"Fallahpoor, M. et al. Segmentation of Liver and Liver Lesions Using Deep Learning 1\u20139 (Physical and Engineering Sciences in Medicine, 2024)."},{"key":"98427_CR28","doi-asserted-by":"crossref","unstructured":"Wang, J. et al. A deep-learning approach for segmentation of liver tumors in magnetic resonance imaging using UNet++. BMC Cncer 23(1),  1060 (2023).","DOI":"10.1186\/s12885-023-11432-x"},{"key":"98427_CR29","doi-asserted-by":"crossref","unstructured":"Umer, J., Irtaza, A. & Nida, N. MACCAI LiTS17 liver tumor segmentation using RetinaNet. In 2020 IEEE 23rd International multitopic conference (INMIC) 1\u20135 (IEEE, 2020).","DOI":"10.1109\/INMIC50486.2020.9318116"},{"key":"98427_CR30","doi-asserted-by":"publisher","unstructured":"Z. et al., Automated diagnosis of liver disorder using multilayer neuro-fuzzy. Int. J. Adv. Appl. Sci. 6(2), 23\u201332. https:\/\/doi.org\/10.21833\/ijaas.2019.02.005 (2019).","DOI":"10.21833\/ijaas.2019.02.005"},{"issue":"9","key":"98427_CR31","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1007\/s11548-020-02222-y","volume":"15","author":"H McGrath","year":"2020","unstructured":"McGrath, H. et al. Manual segmentation versus semi-automated segmentation for quantifying vestibular Schwannoma volume on MRI. Int. J. Comput. Assist. Radiol. Surg. 15(9), 1445\u20131455. https:\/\/doi.org\/10.1007\/s11548-020-02222-y (2020).","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"1","key":"98427_CR32","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00261-021-03262-x","volume":"47","author":"A Mojtahed","year":"2022","unstructured":"Mojtahed, A. et al. Repeatability and reproducibility of deep-learning-based liver volume and Couinaud segment volume measurement tool. Abdom. Radiol. (NY). 47(1), 143\u2013151. https:\/\/doi.org\/10.1007\/s00261-021-03262-x (2022).","journal-title":"Abdom. Radiol. (NY)"},{"key":"98427_CR33","doi-asserted-by":"publisher","first-page":"5837","DOI":"10.2147\/IJN.S466042","volume":"19","author":"Y Wang","year":"2024","unstructured":"Wang, Y. et al. Tumor cell-targeting and tumor microenvironment\u2013responsive nanoplatforms for the multimodal Imaging-Guided photodynamic\/photothermal\/chemodynamic treatment of cervical Cancer. Int. J. Nanomed. 19, 5837\u20135858. https:\/\/doi.org\/10.2147\/IJN.S466042 (2024).","journal-title":"Int. J. Nanomed."},{"issue":"2","key":"98427_CR34","doi-asserted-by":"publisher","first-page":"e223308","DOI":"10.1148\/radiol.223308","volume":"308","author":"VK Vaidhya Venkadesh","year":"2023","unstructured":"Vaidhya Venkadesh, V. K. et al. Prior CT improves deep learning for malignancy risk Estimation of screening-detected pulmonary nodules. Radiol 308(2), e223308. https:\/\/doi.org\/10.1148\/radiol.223308 (2023).","journal-title":"Radiol"},{"issue":"6","key":"98427_CR35","doi-asserted-by":"publisher","first-page":"170","DOI":"10.21833\/ijaas.2024.06.019","volume":"11","author":"AA Alzahrani","year":"2024","unstructured":"Alzahrani, A. A., Ahmed, A. & Raza, A. Content-based medical image retrieval method using multiple pre-trained convolutional neural networks feature extraction models. Int. J. Adv. Appl. Sci. 11(6), 170\u2013177. https:\/\/doi.org\/10.21833\/ijaas.2024.06.019 (2024).","journal-title":"Int. J. Adv. Appl. Sci."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98427-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98427-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98427-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T12:39:47Z","timestamp":1747831187000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-98427-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,21]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["98427"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-98427-9","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,21]]},"assertion":[{"value":"16 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2025","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17697"}}