{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:19:10Z","timestamp":1770275950241,"version":"3.49.0"},"reference-count":92,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,3,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    The liver, a complex and important organ in the human body, is crucial to many physiological processes. For the diagnosis and ongoing monitoring of a wide spectrum of liver diseases, an accurate segmentation of the liver from medical imaging is essential. The importance of liver segmentation in clinical practice is examined in this research, along with the difficulties in attaining accurate segmentation masks, particularly when working with small structures and precise details. This study investigates the performance of ten well-known U-Net models, including Vanilla U-Net, Attention U-Net, V-Net, U-Net 3+, R2U-Net,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/j_jisys-2024-0185_eq_001.png\"\/>\n                        <m:math xmlns:m=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <m:msup>\n                            <m:mrow>\n                              <m:mi mathvariant=\"normal\">U<\/m:mi>\n                            <\/m:mrow>\n                            <m:mrow>\n                              <m:mn>2<\/m:mn>\n                            <\/m:mrow>\n                          <\/m:msup>\n                        <\/m:math>\n                        <jats:tex-math>{{\\rm{U}}}^{2}<\/jats:tex-math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    -Net, U-Net++, Res U-Net, Swin-U-Net, and Trans-U-Net. These variations have become optimal approaches to liver segmentation, each providing certain benefits and addressing particular difficulties. We have conducted this research on computed tomography scan images from three standard datasets, namely, 3DIRCADb, CHAOS, and LiTS datasets. The U-Net architecture has become a mainstay in contemporary research on medical picture segmentation due to its success in preserving contextual information and capturing fine features. The structural and functional characteristics that help it perform well on liver segmentation tasks even with scant annotated data are well highlighted in this study. The code and additional results can be found in the Github\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/akalder\/ComparativeStudyLiverSegmentation\">https:\/\/github.com\/akalder\/ComparativeStudyLiverSegmentation<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1515\/jisys-2024-0185","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T09:02:50Z","timestamp":1741338170000},"source":"Crossref","is-referenced-by-count":3,"title":["An experimental study of U-net variants on liver segmentation from CT scans"],"prefix":"10.1515","volume":"34","author":[{"given":"Akash","family":"Halder","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Jadavpur , Kolkata , West Bengal 700032 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arup","family":"Sau","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Engineering and Management , Kolkata 700032 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Surya","family":"Majumder","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Heritage Institute of Technology , Kolkata , West Bengal 700107 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitrii","family":"Kaplun","sequence":"additional","affiliation":[{"name":"Department of Automation and Control Processes, Saint Petersburg Electrotechnical University \u201cLETI\u201d , St Petersburg 197022 , Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Jadavpur University, Jadavpur , Kolkata , West Bengal 700032 , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"2025122009032181373_j_jisys-2024-0185_ref_001","unstructured":"Pharmacy Images. Labelled diagram of liver \u2223 liver images \u2223 human liver diagram. https:\/\/commons.wikimedia.org\/wiki\/File:Liver_Diagram.svg."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_002","doi-asserted-by":"crossref","unstructured":"Nayantara PV, Kamath S, Manjunath K, Rajagopal K. Computer-aided diagnosis of liver lesions using CT images: A systematic review. Comput Biol Med. 2020;127:104035. 10.1016\/j.compbiomed.2020.104035.","DOI":"10.1016\/j.compbiomed.2020.104035"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_003","doi-asserted-by":"crossref","unstructured":"Asrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151\u201371. 10.1016\/j.jhep.2018.09.014.","DOI":"10.1016\/j.jhep.2018.09.014"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_004","doi-asserted-by":"crossref","unstructured":"Campadelli P, Casiraghi E, Esposito A. Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intel Med. 2009;45(2\u20133):185\u201396. 10.1016\/j.artmed.2008.07.020.","DOI":"10.1016\/j.artmed.2008.07.020"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_005","doi-asserted-by":"crossref","unstructured":"Arakeri MP. Recent advances and future potential of computer aided diagnosis of liver cancer on computed tomography images. In: International Conference on Information Processing. Springer; 2011. p. 246\u201351. 10.1007\/978-3-642-22786-8.","DOI":"10.1007\/978-3-642-22786-8_31"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_006","unstructured":"Cleveland Clinic. Liver cancer. Accessed: December 12, 2023. [Online]. Available: https:\/\/my.clevelandclinic.org\/health\/diseases\/9418-liver-cancer."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_007","unstructured":"Herbert Irving Comprehensive Cancer Center, Columbia University. Liver cancer. Accessed: December 20, 2023. [Online]. Available: https:\/\/www.cancer.columbia.edu\/cancer-types-care\/types\/liver-cancer\/about-liver-cancer."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_008","doi-asserted-by":"crossref","unstructured":"Ansari MY, Abdalla A, Ansari MY, Ansari MI, Malluhi B, Mohanty S, et al. Practical utility of liver segmentation methods in clinical surgeries and interventions. BMC Medical Imaging. 2022;22(1):1\u201317. 10.1186\/s12880-022-00825-2.","DOI":"10.1186\/s12880-022-00869-4"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_009","doi-asserted-by":"crossref","unstructured":"Jayadevappa D, Srinivas Kumar S, Murty D. Medical image segmentation algorithms using deformable models: a review. IETE Tech Rev. 2011;28(3):248\u201355. 10.4103\/0256-4602.81244.","DOI":"10.4103\/0256-4602.81244"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_010","doi-asserted-by":"crossref","unstructured":"Guo X, Schwartz LH, Zhao B. Automatic liver segmentation by integrating fully convolutional networks into active contour models. Med Phys. 2019;46(10):4455\u201369. 10.1002\/mp.13735.","DOI":"10.1002\/mp.13735"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_011","doi-asserted-by":"crossref","unstructured":"Wu W, Zhou Z, Wu S, Zhang Y. Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts. Comput Math Methods Med. 2016;2016:9093721. 10.1155\/2016\/9093721.","DOI":"10.1155\/2016\/9093721"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_012","unstructured":"Thakur P, Madaan N. A survey of image segmentation techniques. Int J Res Comput Appl Robotics. 2014;2(4):158\u201365. https:\/\/api.semanticscholar.org\/CorpusID:212446238."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_013","doi-asserted-by":"crossref","unstructured":"Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2021;18(2):203\u201311. 10.1038\/s41592-020-01008-z.","DOI":"10.1038\/s41592-020-01008-z"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_014","unstructured":"Zhang F, Wang Y, Yang H. Efficient context-aware network for abdominal multi-organ segmentation. 2021. arXiv: http:\/\/arXiv.org\/abs\/arXiv:210910601."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_015","doi-asserted-by":"crossref","unstructured":"Majumder S, Gautam N, Basu A, Sau A, Geem ZW, Sarkar R. MENet: AMitscherlich function based ensemble of CNN models to classify lung cancer using CT scans. Plos One. 2024;19(3):e0298527. 10.1371\/journal.pone.0298527.","DOI":"10.1371\/journal.pone.0298527"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_016","doi-asserted-by":"crossref","unstructured":"Goenka N, Sharma AK, Tiwari S, Singh N, Yadav V, Prabhu S, et al. A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images. Cogent Eng. 2024;11(1):2314872. 10.1080\/23311916.2024.2314872.","DOI":"10.1080\/23311916.2024.2314872"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_017","doi-asserted-by":"crossref","unstructured":"Dsilva LR, Tantri SH, Sampathila N, Mayrose H, Muralidhar Bairy G, Belurkar S, et al. Wavelet scattering-and object detection-based computer vision for identifying dengue from peripheral blood microscopy. Int J Imaging Syst Tech. 2024;34(1):e23020. 10.1002\/ima.23020.","DOI":"10.1002\/ima.23020"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_018","doi-asserted-by":"crossref","unstructured":"Roy A, Pramanik P, Sarkar R. EU2-Net: a parameter efficient ensemble model with attention-aided triple feature fusion for tumor segmentation in breast ultrasound images. IEEE Trans Instrument Measurement. 2024;73:1\u20137. 10.1109\/TIM.2024.3421436.","DOI":"10.1109\/TIM.2024.3421436"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_019","doi-asserted-by":"crossref","unstructured":"Bhattacharyya T, Chatterjee B, Sarkar R, Kundu M. Segmentation of brain MRI using moth-flame optimization with modified cross entropy based fitness function. Multimedia Tools Appl. 2024;83:1\u201322. 10.1007\/s11042-024-18461-z.","DOI":"10.1007\/s11042-024-18461-z"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_020","doi-asserted-by":"crossref","unstructured":"Gautam N, Basu A, Kaplun D, Sarkar R. An ensemble of UNet frameworks for lung nodule segmentation. In: International Conference on Actual Problems of Applied Mathematics and Computer Science. Springer; 2022. p. 450\u201361. 10.1007\/978-3-031-34127-4.","DOI":"10.1007\/978-3-031-34127-4_44"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_021","doi-asserted-by":"crossref","unstructured":"Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8:1\u201374. 10.1186\/s40537-021-00444-8.","DOI":"10.1186\/s40537-021-00444-8"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_022","doi-asserted-by":"crossref","unstructured":"Rozenwald MB, Galitsyna AA, Sapunov GV, Khrameeva EE, Gelfand MS. A machine learning framework for the prediction of chromatin folding in Drosophila using epigenetic features. PeerJ Comput Sci. 2020;6:e307. 10.7717\/peerj-cs.307.","DOI":"10.7717\/peerj-cs.307"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_023","doi-asserted-by":"crossref","unstructured":"Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE. A survey of deep neural network architectures and their applications. Neurocomputing. 2017;234:11\u201326. 10.1016\/j.neucom.2016.12.038.","DOI":"10.1016\/j.neucom.2016.12.038"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_024","doi-asserted-by":"crossref","unstructured":"Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, et al. A survey on deep learning: Algorithms, techniques, and applications. ACM Comput Surveys (CSUR). 2018;51(5):1\u201336. 10.1145\/3234150.","DOI":"10.1145\/3234150"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_025","doi-asserted-by":"crossref","unstructured":"Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, et al. A state-of-the-art survey on deep learning theory and architectures. Electronics. 2019;8(3):292. 10.3390\/electronics8030292.","DOI":"10.3390\/electronics8030292"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_026","doi-asserted-by":"crossref","unstructured":"Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology. 2021;46(1):176\u201390. 10.1038\/s41386-020-0767-z.","DOI":"10.1038\/s41386-020-0767-z"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_027","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18. Springer; 2015. p. 234\u201341. 10.1007\/978-3-319-24574-4.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_028","doi-asserted-by":"crossref","unstructured":"Liu X, Zhao H. Hierarchical feature extraction based on discriminant analysis. Appl Intell. 2019;49:2780\u201392. 10.1007\/s10489-019-01418-3.","DOI":"10.1007\/s10489-019-01418-3"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_029","doi-asserted-by":"crossref","unstructured":"Yue L, Gong X, Li J, Ji H, Li M, Nandi AK. Hierarchical feature extraction for early Alzheimeras disease diagnosis. IEEE Access. 2019;7:93752\u201360. 10.1109\/ACCESS.2019.2926288.","DOI":"10.1109\/ACCESS.2019.2926288"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_030","doi-asserted-by":"crossref","unstructured":"Wang Z, Zhang L, Fang T, Mathiopoulos PT, Tong X, Qu H, et al. A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification. IEEE Trans Geosci Remote Sensing. 2014;53(5):2409\u201325. 10.1109\/TGRS.2014.2359951.","DOI":"10.1109\/TGRS.2014.2359951"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_031","doi-asserted-by":"crossref","unstructured":"Masci J, Meier U, Cire\u015fan D, Schmidhuber J. Stacked convolutional auto-encoders for hierarchical feature extraction. In: Artificial and Machine Learning-ICANN 2011: 21st International Conference on Artificial, Espoo, Finland, June 14-17, 2011, Proceedings, Part I 21. Springer; 2011. p. 52\u201359. 10.1007\/978-3-642-21735-7.","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_032","doi-asserted-by":"crossref","unstructured":"Li H, Wei Y, Li L, Chen CP. Hierarchical feature extraction with local neural response for image recognition. IEEE Trans Cybernetics. 2013;43(2):412\u201324. 10.1109\/TSMCB.2012.2208743.","DOI":"10.1109\/TSMCB.2012.2208743"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_033","doi-asserted-by":"crossref","unstructured":"Song HA, Kim BK, Xuan TL, Lee SY. Hierarchical feature extraction by multi-layer non-negative matrix factorization network for classification task. Neurocomputing. 2015;165:63\u201374. 10.1016\/j.neucom.2014.08.095.","DOI":"10.1016\/j.neucom.2014.08.095"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_034","doi-asserted-by":"crossref","unstructured":"Chandra TB, Verma K, Singh BK, Jain D, Netam SS. Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme. Expert Syst. Appl. 2020;158:113514. 10.1016\/j.eswa.2020.113514.","DOI":"10.1016\/j.eswa.2020.113514"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_035","doi-asserted-by":"crossref","unstructured":"Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C. The importance of skip connections in biomedical image segmentation. In: International Workshop on Deep Learning in Medical Image Analysis, International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. Springer; 2016. p. 179\u201387. 10.1007\/978-3-319-46976-8.","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_036","unstructured":"Orhan AE, Pitkow X. Skip connections eliminate singularities. 2017. arXiv: http:\/\/arXiv.org\/abs\/arXiv:170109175."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_037","doi-asserted-by":"crossref","unstructured":"Tong T, Li G, Liu X, Gao Q. Image super-resolution using dense skip connections. In: Proceedings of the IEEE International Conference on Computer Vision; 2017. p. 4799\u2013807.","DOI":"10.1109\/ICCV.2017.514"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_038","unstructured":"Mao X, Shen C, Yang YB. Image restoration using very deep convolutional encoder\u2013decoder networks with symmetric skip connections. Adv Neural Inform Process Syst. 2016;29. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2016\/file\/0ed9422357395a0d4879191c66f4faa2-Paper.pdf."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_039","doi-asserted-by":"crossref","unstructured":"Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Abergel A, Chabrot P, et al. Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme. Comput Biol Med. 2019;110:42\u201351. 10.1016\/j.compbiomed.2019.04.014.","DOI":"10.1016\/j.compbiomed.2019.04.014"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_040","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, et al. Attention u-net: Learning where to look for the pancreas. 2018. 10.48550\/arXiv.1804.03999."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_041","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE; 2016. p. 565\u201371. 10.1109\/3DV.2016.79.","DOI":"10.1109\/3DV.2016.79"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_042","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, et al. Unet 3.: A full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2020. p. 1055\u20139. 10.1109\/ICASSP40776.2020.9053405.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_043","doi-asserted-by":"crossref","unstructured":"Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. 2018. 10.48550\/arXiv.1802.06955.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_044","doi-asserted-by":"crossref","unstructured":"Qin X, Zhang Z, Huang C, Dehghan M, Zaiane OR, Jagersand M. U2-Net: Going deeper with nested U-structure for salient object detection. Pattern Recognit. 2020;106:107404. 10.48550\/arXiv.2005.09007.","DOI":"10.1016\/j.patcog.2020.107404"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_045","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. Unet++: A nested u-net architecture for medical image segmentation. In: 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, September 20, 2018, Proceedings 4. Springer; 2018. p. 3\u201311. 10.1007\/978-3-030-00889-5.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_046","doi-asserted-by":"crossref","unstructured":"Diakogiannis FI, Waldner F, Caccetta P, Wu C. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens. 2020;162:94\u2013114. 10.1016\/j.isprsjprs.2020.01.013.","DOI":"10.1016\/j.isprsjprs.2020.01.013"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_047","doi-asserted-by":"crossref","unstructured":"Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, et al. Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision. Springer; 2022. p. 205\u201318. 10.1007\/978-3-031-25066-8.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_048","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, et al. Transunet: Transformers make strong encoders for medical image segmentation. 2021. 10.48550\/arXiv.2102.04306."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_049","doi-asserted-by":"crossref","unstructured":"Jiang H, Shi T, Bai Z, Huang L. Ahcnet: An application of attention mechanism and hybrid connection for liver tumor segmentation in ct volumes. IEEE Access. 2019;7:24898\u2013909. 10.1109\/ACCESS.2019.2899608.","DOI":"10.1109\/ACCESS.2019.2899608"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_050","doi-asserted-by":"crossref","unstructured":"Jiang L, Ou J, Liu R, Zou Y, Xie T, Xiao H, et al. RMAU-Net: residual multi-scale attention U-Net for liver and tumor segmentation in CT images. Comput Biol Med. 2023;158:106838. 10.1016\/j.compbiomed.2023.106838.","DOI":"10.1016\/j.compbiomed.2023.106838"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_051","doi-asserted-by":"crossref","unstructured":"Bilic P, Christ P, Li HB, Vorontsov E, Ben-Cohen A, Kaissis G, et al. The liver tumor segmentation benchmark (lits). Med Image Anal. 2023;84:102680. 10.1016\/j.media.2022.102680.","DOI":"10.1016\/j.media.2022.102680"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_052","doi-asserted-by":"crossref","unstructured":"Hille G, Agrawal S, Tummala P, Wybranski C, Pech M, Surov A, et al. Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers. Comput Methods Programs Biomed. 2023;240:107647. 10.1016\/j.cmpb.2023.107647.","DOI":"10.1016\/j.cmpb.2023.107647"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_053","doi-asserted-by":"crossref","unstructured":"Li L, Ma H. Rdctrans U-Net: A hybrid variable architecture for liver CT image segmentation. Sensors. 2022;22(7):2452. 10.3390\/s22072452.","DOI":"10.3390\/s22072452"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_054","doi-asserted-by":"crossref","unstructured":"Xie S, Girshick R, Doll\u00e1r P, Tu Z, He K. Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 1492\u2013500. 10.48550\/arXiv.1611.05431.","DOI":"10.1109\/CVPR.2017.634"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_055","doi-asserted-by":"crossref","unstructured":"Jiang J, Peng Y, Hou Q, Wang J. MDCF_Net: A Multi-dimensional hybrid network for liver and tumor segmentation from CT. Biocybernetics Biomed Eng. 2023;43:494\u2013506. 10.1016\/j.bbe.2023.04.004.","DOI":"10.1016\/j.bbe.2023.04.004"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_056","doi-asserted-by":"crossref","unstructured":"Han JJ, Zhang X, Cai X, Pan R, Xiao L, Nan Y, et al. Deep learning model based on CNN-former in the diagnosis and detection of liver fibrosis. Research Square. 2023. 10.21203\/rs.3.rs-2869666\/v1.","DOI":"10.21203\/rs.3.rs-2869666\/v1"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_057","doi-asserted-by":"crossref","unstructured":"Zhang J, Liu Y, Wu Q, Wang Y, Liu Y, Xu X, et al. SWTRU: star-shaped window transformer reinforced U-net for medical image segmentation. Comput Biol Med. 2022;150:105954. 10.1016\/j.compbiomed.2022.105954.","DOI":"10.1016\/j.compbiomed.2022.105954"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_058","doi-asserted-by":"crossref","unstructured":"Mulay S, Deepika G, Jeevakala S, Ram K, Sivaprakasam M. Liver segmentation from multimodal images using HED-mask R-CNN. In: Multiscale Multimodal Medical Imaging: First International Workshop, MMMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 1. Springer; 2020. p. 68\u201375. 10.1007\/978-3-030-37969-8.","DOI":"10.1007\/978-3-030-37969-8_9"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_059","doi-asserted-by":"crossref","unstructured":"Roy SS, Roy S, Mukherjee P, Roy AH. An automated liver tumour segmentation and classification model by deep learning based approaches. Comput Methods Biomech Biomed Eng Imag Vis. 2023;11(3):638\u201350. 10.1080\/21681163.2022.2099300.","DOI":"10.1080\/21681163.2022.2099300"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_060","doi-asserted-by":"crossref","unstructured":"Chi J, Han X, Wu C, Wang H, Ji P. X-Net: Multi-branch UNet-like network for liver and tumor segmentation from 3D abdominal CT scans. Neurocomputing. 2021;459:81\u201396. 10.1016\/j.neucom.2021.06.021..","DOI":"10.1016\/j.neucom.2021.06.021"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_061","doi-asserted-by":"crossref","unstructured":"Manjunath RV, Kwadiki K. Modified U-NET on CT images for automatic segmentation of liver and its tumor. Biomed Eng Adv. 2022;4:100043. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2667099222000196.","DOI":"10.1016\/j.bea.2022.100043"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_062","doi-asserted-by":"crossref","unstructured":"Khan RA, Luo Y, Wu FX. RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation. Artif Intel Med. 2022;124:102231. 10.1016\/j.artmed.2021.102231.","DOI":"10.1016\/j.artmed.2021.102231"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_063","doi-asserted-by":"crossref","unstructured":"Kavur AE, Gezer NS, Bar\u00edi\u015f M, Aslan S, Conze PH, Groza V, et al. CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation. Med Image Anal. 2021;69:101950. 10.1016\/j.media.2020.101950.","DOI":"10.1016\/j.media.2020.101950"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_064","doi-asserted-by":"crossref","unstructured":"Won YD, Na MK, Kim CH, Kim JM, Cheong JH, Ryu JI, et al. The frontal skull Hounsfield unit value can predict ventricular enlargement in patients with subarachnoid haemorrhage. Sci Reports. 2018;8(1):10178. 10.1038\/s41598-018-28471-1.","DOI":"10.1038\/s41598-018-28471-1"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_065","doi-asserted-by":"crossref","unstructured":"Ishihara H, Oka F, Kawano R, Shinoyama M, Nishimoto T, Kudomi S, et al. Hounsfield unit value of interpeduncular cistern hematomas can predict symptomatic vasospasm. Stroke. 2020;51(1):143\u20138. 10.1161\/STROKEAHA.119.026962.","DOI":"10.1161\/STROKEAHA.119.026962"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_066","unstructured":"DenOtter TD, Schubert J. Hounsfield unit. StatPearls; 2019."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_067","unstructured":"Thomsen V, Schatzlein D, Mercuro D. Tutorial: attenuation of X-rays by matter. Spectroscopy. 2005;20(9)."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_068","doi-asserted-by":"crossref","unstructured":"DeMarco JJ, Suortti P. Effect of scattering on the attenuation of X rays. Phys Rev B. 1971;4(4):1028. 10.1103\/PhysRevB.4.1028.","DOI":"10.1103\/PhysRevB.4.1028"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_069","doi-asserted-by":"crossref","unstructured":"Artem\u2019ev V. Attenuation of X rays by ultradisperse media. Tech Phys Lett. 1997;23:212\u20133. 10.1134\/1.1261601.","DOI":"10.1134\/1.1261601"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_070","doi-asserted-by":"crossref","unstructured":"Molteni R. Prospects and challenges of rendering tissue density in Hounsfield units for cone beam computed tomography. Oral Surg Oral Med Oral Pathol Oral Radiol. 2013;116(1):105\u201319. 10.1016\/j.oooo.2013.04.013.","DOI":"10.1016\/j.oooo.2013.04.013"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_071","doi-asserted-by":"crossref","unstructured":"Schreiber JJ, Anderson PA, Rosas HG, Buchholz AL, Au AG. Hounsfield units for assessing bone mineral density and strength: a tool for osteoporosis management. JBJS. 2011;93(11):1057\u201363. 10.2106\/JBJS.J.00160.","DOI":"10.2106\/JBJS.J.00160"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_072","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision; 2015. p. 1026\u201334.","DOI":"10.1109\/ICCV.2015.123"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_073","doi-asserted-by":"crossref","unstructured":"Jiang T, Cheng J. Target recognition based on CNN with LeakyReLU and PReLU activation functions. In: 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE; 2019. p. 718\u201322.","DOI":"10.1109\/SDPC.2019.00136"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_074","doi-asserted-by":"crossref","unstructured":"Crnjanski J, Krsti\u0107 M, Totovi\u0107 A, Pleros N, Gvozdi\u0107 D. Adaptive sigmoid-like and PReLU activation functions for all-optical perceptron. Optics Letters. 2021;46(9):2003\u20136. 10.1364\/OL.422930.","DOI":"10.1364\/OL.422930"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_075","unstructured":"Jang E, Gu S, Poole B. Categorical reparameterization with gumbel-softmax. 2016. arXiv: http:\/\/arXiv.org\/abs\/arXiv:161101144."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_076","unstructured":"Liu W, Wen Y, Yu Z, Yang M. Large-margin softmax loss for convolutional neural networks. 2016. arXiv: http:\/\/arXiv.org\/abs\/arXiv:161202295."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_077","doi-asserted-by":"crossref","unstructured":"Han J, Moraga C. The influence of the sigmoid function parameters on the speed of backpropagation learning. In: International Workshop on Artificial Neural Networks. Springer; 1995. p. 195\u2013201. 10.1007\/3-540-59497-3.","DOI":"10.1007\/3-540-59497-3_175"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_078","doi-asserted-by":"crossref","unstructured":"Yin X, Goudriaan J, Lantinga EA, Vos J, Spiertz HJ. A flexible sigmoid function of determinate growth. Ann Bot. 2003;91(3):361\u201371. 10.1093\/aob\/mcg091.","DOI":"10.1093\/aob\/mcg029"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_079","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision; 2021. p. 10012\u201322. 10.48550\/arXiv.2103.14030.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_080","doi-asserted-by":"crossref","unstructured":"Sun C, Xu A, Liu D, Xiong Z, Zhao F, Ding W. Deep learning-based classification of liver cancer histopathology images using only global labels. IEEE J Biomed Health Inform. 2019;24(6):1643\u201351. 10.1109\/JBHI.2019.2949837.","DOI":"10.1109\/JBHI.2019.2949837"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_081","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang X, Wang G, Zhang Y, Shi X, Dai H, et al. TransFusionNet: Semantic and spatial features fusion framework for liver tumor and vessel segmentation under JetsonTX2. IEEE J Biomed Health Informatics. 2022;27(3):1173\u201384. 10.1109\/JBHI.2022.3207233.","DOI":"10.1109\/JBHI.2022.3207233"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_082","doi-asserted-by":"crossref","unstructured":"Zhao R, Qian B, Zhang X, Li Y, Wei R, Liu Y, et al. Rethinking dice loss for medical image segmentation. In: 2020 IEEE International Conference on Data Mining (ICDM). IEEE; 2020. p. 851\u201360. 10.1109\/ICDM50108.2020.00094.","DOI":"10.1109\/ICDM50108.2020.00094"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_083","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv: http:\/\/arXiv.org\/abs\/arXiv:14091556."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_084","unstructured":"Dozat T. Incorporating Nesterov momentum into Adam. Caribe Hilton, San Juan, Puerto Rico: ICLR2016; 2016. https:\/\/openreview.net\/forum?id=OM0jvwB8jIp57ZJjtNEZ&noteId=nx924kDvKc7lP3z2iomv."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_085","unstructured":"Kingma DP, Ba J. Adam: A method for stochastic optimization. 2014. arXiv: http:\/\/arXiv.org\/abs\/arXiv:14126980."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_086","unstructured":"Xie X, Zhou P, Li H, Lin Z, Yan S. Adan: Adaptive Nesterov momentum algorithm for faster optimizing deep models. 2022. arXiv: http:\/\/arXiv.org\/abs\/arXiv:220806677."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_087","doi-asserted-by":"crossref","unstructured":"Tang S, Shen C, Wang D, Li S, Huang W, Zhu Z. Adaptive deep feature learning network with Nesterov momentum and its application to rotating machinery fault diagnosis. Neurocomputing. 2018;305:1\u201314. 10.1016\/j.neucom.2018.04.048.","DOI":"10.1016\/j.neucom.2018.04.048"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_088","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision; 2017. p. 618\u201326. 10.1109\/ICCV.2017.74.","DOI":"10.1109\/ICCV.2017.74"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_089","doi-asserted-by":"crossref","unstructured":"Kim YJ, Jang H, Lee K, Park S, Min SG, Hong C, et al. PAIP 2019: Liver cancer segmentation challenge. Med Image Anal. 2021;67: 101854. 10.1016\/j.media.2020.101854.","DOI":"10.1016\/j.media.2020.101854"},{"key":"2025122009032181373_j_jisys-2024-0185_ref_090","unstructured":"Soler L, Hostettler A, Agnus V, Charnoz A, Fasquel JB, Moreau J, et al. 3D image reconstruction for comparison of algorithm database: A patient specific anatomical and medical image database. IRCAD, Strasbourg, France, Tech. Rep. 2010."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_091","unstructured":"Kavur AE, Selver MA, Dicle O, Bar\u0131\u015f M,\u00a0 Gezer NS. CHAOS - Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Zenodo. 2019. 10.5281\/zenodo.3362844."},{"key":"2025122009032181373_j_jisys-2024-0185_ref_092","doi-asserted-by":"crossref","unstructured":"Kavur AE, Gezer NS, Bar\u0131\u015f M, \u015eahin Y, \u00d6zkan S, Baydar B, et al. Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors. Diagn Inter Radiol. 2020;26:11\u201321. 10.5152\/dir.2019.19025.","DOI":"10.5152\/dir.2019.19025"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2024-0185\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2024-0185\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T09:09:45Z","timestamp":1766221785000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2024-0185\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,1]]},"references-count":92,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,7]]},"published-print":{"date-parts":[[2025,3,7]]}},"alternative-id":["10.1515\/jisys-2024-0185"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2024-0185","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,1]]},"article-number":"20240185"}}