{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T19:51:17Z","timestamp":1779306677673,"version":"3.51.4"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T00:00:00Z","timestamp":1728864000000},"content-version":"vor","delay-in-days":287,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>Liver cancer is one of the leading causes of cancer death in the world, and early diagnosis is important. However, the similarity in shape, texture, and intensity values between the liver, tumors, and other neighboring organs such as the heart, spleen, stomach, and kidneys often complicates visual differentiation. Manual identification of tumors in the liver is time\u2010consuming, intricate, and susceptible to errors with potential repercussions for patient care. While machine learning\u2013based approaches have emerged for liver organ recognition and segmenting the tumor, they continue to face challenges related to recognition accuracy and the inability to distinguish tumors of varied sizes. To solve the problems, a multiattention network made up of cascaded ResUNet and U\u2010Net with attention mechanisms was proposed in this study. We investigated liver tumor segmentation with various configurations of U\u2010Net, ResUNet, U\u2010Net with attention mechanisms, and ResUNet with attention mechanisms on augmented and nonaugmented data. We used the 3Dircadb dataset for training and validation purposes, and the proposed method was evaluated on dice score, intersection of union (IoU), recall, and precision. The performance metrics achieved with this method on the dataset are as follows: approximately 0.89 for the dice coefficient, 0.90 for IoU, 0.93 for recall, and 0.96 for precision in the case of liver segmentation without data augmentation and 0.92, 0.90, 0.92, and 0.94, respectively, for dice score, IoU, recall, and precision with data augmentation. For tumor segmentation, the metrics include 0.70 for dice coefficient, 0.61 for IoU, 0.91 for recall, and 0.94 for precision when the data were augmented but 0.83 for dice score, 0.78 for IoU, and 0.89 and 0.90, respectively, for recall and precision.<\/jats:p>","DOI":"10.1155\/2024\/8365349","type":"journal-article","created":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T13:03:42Z","timestamp":1728911022000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Multiattention ResUNet and Modified U\u2010Net Architecture for Liver Tumor Segmentation"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2798-4524","authenticated-orcid":false,"given":"Justice Kwame","family":"Appati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6397-5647","authenticated-orcid":false,"given":"Nathanael Ayirebaje","family":"Azuponga","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8332-978X","authenticated-orcid":false,"given":"Leonard Mensah","family":"Boante","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7754-6613","authenticated-orcid":false,"given":"Joseph Agyeapong","family":"Mensah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105095"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104305"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/ijc.33232"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/eurpub\/ckz216"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13244-017-0558-1"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bea.2022.100043"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106501"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102023"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3047861"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12132796"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106268"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40763-5_50"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.05.004"},{"key":"e_1_2_9_15_2","first-page":"415","article-title":"Automatic Liver and Lesion Segmentation in Ct Using Cascaded Fully Convolutional Neural Networks and 3d Conditional Random Fields","author":"Christ P. F.","year":"2016","journal-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/978-3-319-46976-8_19","article-title":"The Importance of Skip Connections in Biomedical Image Segmentation","author":"Drozdzal M.","year":"2016","journal-title":"Deep Learning and Data Labeling for Medical Applications"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1109\/3DV.2016.79","article-title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","author":"Milletari F.","year":"2016","journal-title":"2016 Fourth International Conference on 3D Vision (3DV)"},{"key":"e_1_2_9_18_2","first-page":"83","article-title":"Recurrent Fully Convolutional Neural Networks for Multi-Slice Mri Cardiac Segmentation","author":"Malhotra P.","year":"2016","journal-title":"Reconstruction, Segmentation, and Analysis of Medical Images"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/978-3-319-46976-8_9","article-title":"Fully Convolutional Network for Liver Segmentation and Lesions Detection","author":"Ben-Cohen A.","year":"2016","journal-title":"Deep Learning and Data Labeling for Medical Applications"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2017.03.008"},{"key":"e_1_2_9_22_2","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1109\/ISBI.2018.8363817","article-title":"Liver Lesion Segmentation Informed by Joint Liver Segmentation","author":"Vorontsov E.","year":"2018","journal-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)"},{"key":"e_1_2_9_23_2","unstructured":"ChlebusG. MeineH. MoltzJ. H. andSchenkA. Neural Network-Based Automatic Liver Tumor Segmentation With Random Forest-Based Candidate Filtering 2017."},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2018.2845918"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1109\/BIBM47256.2019.8983266","article-title":"CU-Net: Cascaded U-Net Model for Automated Liver and Lesion Segmentation and Summarization","author":"Albishri A. A.","year":"2019","journal-title":"2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) CU-Net"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2985671"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1109\/EUVIP53989.2022.9922871"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10113794"}],"container-title":["Applied Computational Intelligence and Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2024\/8365349","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T13:03:50Z","timestamp":1728911030000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2024\/8365349"}},"subtitle":[],"editor":[{"given":"Vishnu Srinivasa Murthy","family":"Yarlagadda","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":28,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1155\/2024\/8365349"],"URL":"https:\/\/doi.org\/10.1155\/2024\/8365349","archive":["Portico"],"relation":{},"ISSN":["1687-9724","1687-9732"],"issn-type":[{"value":"1687-9724","type":"print"},{"value":"1687-9732","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2024-05-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-13","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"8365349"}}