{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:21:03Z","timestamp":1758349263323,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049360"},{"type":"electronic","value":"9783032049377"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04937-7_52","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:17Z","timestamp":1758260417000},"page":"547-557","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatial-Temporal Memory Filtering SAM for\u00a0Lesion Segmentation in\u00a0Breast Ultrasound Videos"],"prefix":"10.1007","author":[{"given":"Zhengzheng","family":"Tu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunpeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoxue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"52_CR1","doi-asserted-by":"crossref","unstructured":"Sung, H., et al.: Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Can. J. Clin. 71(3), 209\u2013249 (2021)","DOI":"10.3322\/caac.21660"},{"key":"52_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/978-3-030-59725-2_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"R Zhang","year":"2020","unstructured":"Zhang, R., Li, G., Li, Z., Cui, S., Qian, D., Yu, Y.: Adaptive context selection for polyp segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12266, pp. 253\u2013262. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59725-2_25"},{"key":"52_CR3","first-page":"11781","volume":"34","author":"HK Cheng","year":"2021","unstructured":"Cheng, H.K., Tai, Y.W., Tang, C.K.: Rethinking space-time networks with improved memory coverage for efficient video object segmentation. Adv. Neural. Inf. Process. Syst. 34, 11781\u201311794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"52_CR4","doi-asserted-by":"crossref","unstructured":"Cheng, H.K., Schwing, A.G.: Xmem: long-term video object segmentation with an atkinson-shiffrin memory model. In: European Conference on Computer Vision, pp. 640\u2013658. Springer (2022)","DOI":"10.1007\/978-3-031-19815-1_37"},{"key":"52_CR5","doi-asserted-by":"crossref","unstructured":"Ji, G.P., et al.: Progressively normalized self-attention network for video polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 142\u2013152. Springer (2021)","DOI":"10.1007\/978-3-030-87193-2_14"},{"key":"52_CR6","doi-asserted-by":"crossref","unstructured":"Lin, J., et al.: Shifting more attention to breast lesion segmentation in ultrasound videos. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 497\u2013507. Springer (2023)","DOI":"10.1007\/978-3-031-43898-1_48"},{"key":"52_CR7","doi-asserted-by":"crossref","unstructured":"Zhuang, Z., Li, N., Joseph Raj, A.N., Mahesh, V.G.V., Qiu, S.: An RDAU-NET model for lesion segmentation in breast ultrasound images. PloS one 14(8), e0221535 (2019)","DOI":"10.1371\/journal.pone.0221535"},{"key":"52_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Que, D., Tan, J., Xiao, Y., Yu, Y.: Automated breast lesion segmentation from ultrasound images based on ppu-net. In: 2019 International Conference on Medical Imaging Physics and Engineering (ICMIPE), pp. 1\u20134. IEEE (2019)","DOI":"10.1109\/ICMIPE47306.2019.9098209"},{"key":"52_CR9","doi-asserted-by":"crossref","unstructured":"Fan, D.P., et al.: Pranet: parallel reverse attention network for polyp segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 263\u2013273. Springer (2020)","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"52_CR10","doi-asserted-by":"publisher","first-page":"102027","DOI":"10.1016\/j.bspc.2020.102027","volume":"61","author":"M Byra","year":"2020","unstructured":"Byra, M., et al.: Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomed. Signal Process. Control 61, 102027 (2020)","journal-title":"Biomed. Signal Process. Control"},{"key":"52_CR11","doi-asserted-by":"crossref","unstructured":"Tu, Z., Zhu, Z., Duan, Y., Jiang, B., Wang, Q., Zhang, C.: A spatial-temporal progressive fusion network for breast lesion segmentation in ultrasound videos. arXiv preprint arXiv:2403.11699 (2024)","DOI":"10.1109\/TMM.2025.3599028"},{"key":"52_CR12","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"52_CR13","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: a nested u-net architecture for medical image segmentation. In: DLMIA 2018, ML-CDS 2018, pp. 3\u201311. Springer (2018)","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"52_CR14","doi-asserted-by":"crossref","unstructured":"Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., Lin, Y.L.: Hardnet: a low memory traffic network. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3552\u20133561 (2019)","DOI":"10.1109\/ICCV.2019.00365"},{"key":"52_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-030-87193-2_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Zhao","year":"2021","unstructured":"Zhao, X., Zhang, L., Lu, H.: Automatic polyp segmentation via multi-scale subtraction network. In: de Bruijne, M., et al. (eds.) MICCAI 2021. LNCS, vol. 12901, pp. 120\u2013130. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87193-2_12"},{"key":"52_CR16","doi-asserted-by":"crossref","unstructured":"Tomar, N.K., Shergill, A., Rieders, B., Bagci, U., Jha, D.: TransResU-net: transformer based ResU-Net for real-time colonoscopy polyp segmentation. arXiv preprint arXiv:2206.08985 (2022)","DOI":"10.1109\/EMBC40787.2023.10340572"},{"key":"52_CR17","doi-asserted-by":"crossref","unstructured":"Wang, H., Cao, P., Wang, J., Zaiane, O.R.: Uctransnet: rethinking the skip connections in u-net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 3, pp. 2441\u20132449 (2022)","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"52_CR18","unstructured":"Lin, X., Xiang, Y., Zhang, L., Yang, X., Yan, Z., Yu, L.: SAMUS: adapting segment anything model for clinically-friendly and generalizable ultrasound image segmentation. arXiv preprint arXiv:2309.06824 (2023)"},{"key":"52_CR19","doi-asserted-by":"crossref","unstructured":"Oh, S.W., Lee, J.Y., Xu, N., Kim, S.J.: Video object segmentation using space-time memory networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9226\u20139235 (2019)","DOI":"10.1109\/ICCV.2019.00932"},{"key":"52_CR20","first-page":"3430","volume":"33","author":"Y Liang","year":"2020","unstructured":"Liang, Y., Li, X., Jafari, N., Chen, J.: Video object segmentation with adaptive feature bank and uncertain-region refinement. Adv. Neural. Inf. Process. Syst. 33, 3430\u20133441 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"52_CR21","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","volume":"5","author":"S Liu","year":"2019","unstructured":"Liu, S., et al.: Deep learning in medical ultrasound analysis: a review. Engineering 5(2), 261\u2013275 (2019)","journal-title":"Engineering"},{"key":"52_CR22","doi-asserted-by":"crossref","unstructured":"Sood, R., et al.: Ultrasound for breast cancer detection globally: a systematic review and meta-analysis. J. Global Oncol. (2019)","DOI":"10.1200\/JGO.19.00127"},{"key":"52_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, M., et al.: Dynamic context-sensitive filtering network for video salient object detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1553\u20131563 (2021)","DOI":"10.1109\/ICCV48922.2021.00158"},{"key":"52_CR24","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1109\/TMM.2023.3264883","volume":"26","author":"Y Su","year":"2023","unstructured":"Su, Y., Deng, J., Sun, R., Lin, G., Su, H., Wu, Q.: A unified transformer framework for group-based segmentation: co-segmentation, co-saliency detection and video salient object detection. IEEE Trans. Multimedia 26, 313\u2013325 (2023)","journal-title":"IEEE Trans. Multimedia"},{"key":"52_CR25","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"52_CR26","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Cheng, M.M., Liu, Y., Li, T., Borji, A.: Structure-measure: a new way to evaluate foreground maps. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4548\u20134557 (2017)","DOI":"10.1109\/ICCV.2017.487"},{"issue":"6","key":"52_CR27","first-page":"5","volume":"6","author":"DP Fan","year":"2021","unstructured":"Fan, D.P., Ji, G.P., Qin, X., Cheng, M.M.: Cognitive vision inspired object segmentation metric and loss function. Scientia Sinica Informationis 6(6), 5 (2021)","journal-title":"Scientia Sinica Informationis"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_52","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:31Z","timestamp":1758260431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_52","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}