{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:45:48Z","timestamp":1784997948915,"version":"3.55.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_38","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"403-413","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mask-Enhanced Segment Anything Model for\u00a0Tumor Lesion Semantic Segmentation"],"prefix":"10.1007","author":[{"given":"Hairong","family":"Shi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songhao","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaofei","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanbin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangxing","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Si","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"38_CR1","doi-asserted-by":"publisher","first-page":"179424","DOI":"10.1109\/ACCESS.2020.3026658","volume":"8","author":"A Abdollahi","year":"2020","unstructured":"Abdollahi, A., Pradhan, B., Alamri, A.: VNet: an end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data. IEEE Access 8, 179424\u2013179436 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"38_CR2","doi-asserted-by":"publisher","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","volume":"13","author":"M Antonelli","year":"2022","unstructured":"Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 4128 (2022)","journal-title":"Nat. Commun."},{"key":"38_CR3","unstructured":"Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)"},{"key":"38_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680","volume":"84","author":"P Bilic","year":"2023","unstructured":"Bilic, P., et al.: The liver tumor segmentation benchmark (LITS). Med. Image Anal. 84, 102680 (2023)","journal-title":"Med. Image Anal."},{"key":"38_CR5","doi-asserted-by":"crossref","unstructured":"Bui, N.T., Hoang, D.H., Tran, M.T., Le, N.: SAM3D: segment anything model in volumetric medical images. arXiv preprint arXiv:2309.03493 (2023)","DOI":"10.1109\/ISBI56570.2024.10635844"},{"key":"38_CR6","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"38_CR7","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: SAM fails to segment anything?\u2013SAM-adapter: adapting SAM in underperformed scenes: camouflage, shadow, and more. arXiv preprint arXiv:2304.09148 (2023)","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"38_CR8","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","volume":"41","author":"Q Dou","year":"2017","unstructured":"Dou, Q., et al.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40\u201354 (2017)","journal-title":"Med. Image Anal."},{"key":"38_CR9","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1007\/978-3-319-91008-6_63","volume-title":"Advances in Computer Science for Engineering and Education","author":"Y Gordienko","year":"2019","unstructured":"Gordienko, Y., et al.: Deep learning with lung segmentation and bone shadow exclusion techniques for chest X-ray analysis of lung cancer. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds.) ICCSEEA 2018. AISC, vol. 754, pp. 638\u2013647. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-319-91008-6_63"},{"key":"38_CR10","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/978-3-031-08999-2_22","volume-title":"BrainLes 2021","author":"A Hatamizadeh","year":"2021","unstructured":"Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) BrainLes 2021. LNCS, vol. 12962, pp. 272\u2013284. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-031-08999-2_22"},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"38_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821","volume":"67","author":"N Heller","year":"2021","unstructured":"Heller, N., et al.: The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: results of the KiTS19 challenge. Med. Image Anal. 67, 101821 (2021)","journal-title":"Med. Image Anal."},{"key":"38_CR13","doi-asserted-by":"crossref","unstructured":"Isensee, F., et al.: nnU-Net: self-adapting framework for U-net-based medical image segmentation. arXiv preprint arXiv:1809.10486 (2018)","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"38_CR14","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"38_CR15","unstructured":"Lei, W., Wei, X., Zhang, X., Li, K., Zhang, S.: Medlsam: localize and segment anything model for 3D medical images. arXiv preprint arXiv:2306.14752 (2023)"},{"key":"38_CR16","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"issue":"1","key":"38_CR17","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J., He, Y., Li, F., Han, L., You, C., Wang, B.: Segment anything in medical images. Nat. Commun. 15(1), 654 (2024)","journal-title":"Nat. Commun."},{"key":"38_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"38_CR19","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19, 221\u2013248 (2017)","journal-title":"Annu. Rev. Biomed. Eng."},{"issue":"11","key":"38_CR20","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.3390\/diagnostics13111947","volume":"13","author":"P Shi","year":"2023","unstructured":"Shi, P., Qiu, J., Abaxi, S.M.D., Wei, H., Lo, F.P.W., Yuan, W.: Generalist vision foundation models for medical imaging: a case study of segment anything model on zero-shot medical segmentation. Diagnostics 13(11), 1947 (2023)","journal-title":"Diagnostics"},{"key":"38_CR21","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"38_CR22","unstructured":"Wang, H., et al.: SAM-Med3D. arXiv preprint arXiv:2310.15161 (2023)"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Wei, X., Cao, J., Jin, Y., Lu, M., Wang, G., Zhang, S.: I-MedSAM: implicit medical image segmentation with segment anything. arXiv preprint arXiv:2311.17081 (2023)","DOI":"10.1007\/978-3-031-72684-2_6"},{"key":"38_CR24","unstructured":"Wu, J., et al.: Medical SAM adapter: adapting segment anything model for medical image segmentation. arXiv preprint arXiv:2304.12620 (2023)"},{"key":"38_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/978-3-319-67389-9_32","volume-title":"Machine Learning in Medical Imaging","author":"G Zeng","year":"2017","unstructured":"Zeng, G., Yang, X., Li, J., Yu, L., Heng, P.-A., Zheng, G.: 3D U-net with multi-level deep supervision: fully automatic segmentation of proximal femur in 3D MR images. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 274\u2013282. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_32"},{"key":"38_CR26","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","volume":"108","author":"W Zhang","year":"2015","unstructured":"Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuroimage 108, 214\u2013224 (2015)","journal-title":"Neuroimage"},{"key":"38_CR27","unstructured":"Zhou, H.Y., Guo, J., Zhang, Y., Yu, L., Wang, L., Yu, Y.: nnFormer: interleaved transformer for volumetric segmentation. arXiv preprint arXiv:2109.03201 (2021)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T04:27:55Z","timestamp":1732854475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}