{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T11:05:48Z","timestamp":1768993548249,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556335","type":"print"},{"value":"9789819556342","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-5634-2_7","type":"book-chapter","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:08Z","timestamp":1768944188000},"page":"91-105","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CSP-SAM: CNN-Enhanced and\u00a0Self-prompting SAM for\u00a0Ultrasound Anatomical Structure Segmentation"],"prefix":"10.1007","author":[{"given":"Chen","family":"Yin","sequence":"first","affiliation":[]},{"given":"Xingbo","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Ying","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Bocheng","family":"Liang","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Zhe","family":"Jin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Asad, M., Dorent, R., Vercauteren, T.: FastGeodis: fast generalised geodesic distance transform. arXiv preprint arXiv:2208.00001 (2022)","DOI":"10.21105\/joss.04532"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Cao, H., et al.: Swin-Unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218. Springer (2022)","DOI":"10.1007\/978-3-031-25066-8_9"},{"issue":"3","key":"7_CR3","doi-asserted-by":"publisher","first-page":"211","DOI":"10.5582\/bst.2023.01128","volume":"17","author":"F Chen","year":"2023","unstructured":"Chen, F., Chen, L., Han, H., Zhang, S., Zhang, D., Liao, H.: The ability of segmenting anything model (SAM) to segment ultrasound images. Biosci. Trends 17(3), 211\u2013218 (2023)","journal-title":"Biosci. Trends"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Z., Xu, Q., Liu, X., Yuan, Y.: Un-SAM: universal prompt-free segmentation for generalized nuclei images. arXiv preprint arXiv:2402.16663 (2024)","DOI":"10.1016\/j.media.2025.103607"},{"key":"7_CR6","unstructured":"Cheng, J., et\u00a0al.: SAM-Med2d. arXiv preprint arXiv:2308.16184 (2023)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, Z., et al.: Unleashing the potential of SAM for medical adaptation via hierarchical decoding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3511\u20133522 (2024)","DOI":"10.1109\/CVPR52733.2024.00337"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Deng, X., Wu, H., Zeng, R., Qin, J.: Memsam: taming segment anything model for echocardiography video segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9622\u20139631 (2024)","DOI":"10.1109\/CVPR52733.2024.00919"},{"issue":"4","key":"7_CR9","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1109\/JBHI.2019.2948316","volume":"24","author":"J Dong","year":"2019","unstructured":"Dong, J., et al.: A generic quality control framework for fetal ultrasound cardiac four-chamber planes. IEEE J. Biomed. Health Inform. 24(4), 931\u2013942 (2019)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102629","volume":"83","author":"MC Fiorentino","year":"2023","unstructured":"Fiorentino, M.C., Villani, F.P., Di Cosmo, M., Frontoni, E., Moccia, S.: A review on deep-learning algorithms for fetal ultrasound-image analysis. Med. Image Anal. 83, 102629 (2023)","journal-title":"Med. Image Anal."},{"issue":"1","key":"7_CR11","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s41746-019-0216-8","volume":"3","author":"A Ghorbani","year":"2020","unstructured":"Ghorbani, A., et al.: Deep learning interpretation of echocardiograms. NPJ Digit. Med. 3(1), 10 (2020)","journal-title":"NPJ Digit. Med."},{"key":"7_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102042","volume":"71","author":"L Guo","year":"2021","unstructured":"Guo, L., et al.: Dual attention enhancement feature fusion network for segmentation and quantitative analysis of paediatric echocardiography. Med. Image Anal. 71, 102042 (2021)","journal-title":"Med. Image Anal."},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"He, W., et al.: APSeg: auto-prompt network for cross-domain few-shot semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23762\u201323772 (2024)","DOI":"10.1109\/CVPR52733.2024.02243"},{"key":"7_CR14","unstructured":"Hu, E.J., et al.: LoRA: low-rank adaptation of large language models. In: ICLR, vol. 1, no. 2, p. 3 (2022)"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Hu, J., Zhuo, W., Cheng, J., Liu, Y., Xue, W., Ni, D.: EchoONE: segmenting multiple echocardiography planes in one model. arXiv preprint arXiv:2412.02993 (2024)","DOI":"10.1109\/CVPR52734.2025.00491"},{"key":"7_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103061","volume":"92","author":"Y Huang","year":"2024","unstructured":"Huang, Y., et al.: Segment anything model for medical images? Med. Image Anal. 92, 103061 (2024)","journal-title":"Med. Image Anal."},{"key":"7_CR17","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"issue":"9","key":"7_CR18","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198\u20132210 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7_CR19","unstructured":"Li, C., Khanduri, P., Qiang, Y., Sultan, R.I., Chetty, I., Zhu, D.: Auto-prompting SAM for mobile friendly 3D medical image segmentation, 2. arXiv preprint arXiv:2308.14936 (2023)"},{"key":"7_CR20","doi-asserted-by":"crossref","unstructured":"Li, C., Sultan, R.I., Khanduri, P., Qiang, Y., Indrin, C., Zhu, D.: AutoProSAM: automated prompting SAM for 3D multi-organ segmentation. In: 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 3570\u20133580. IEEE (2025)","DOI":"10.1109\/WACV61041.2025.00352"},{"key":"7_CR21","unstructured":"Li, Y., Zhang, L., Liang, Y., Xie, P.: AM-SAM: automated prompting and mask calibration for segment anything model. arXiv preprint arXiv:2410.09714 (2024)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Lin, X., Xiang, Y., Yu, L., Yan, Z.: Beyond adapting SAM: towards end-to-end ultrasound image segmentation via auto prompting. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 24\u201334. Springer (2024)","DOI":"10.1007\/978-3-031-72111-3_3"},{"key":"7_CR24","unstructured":"Lin, Y., et al.: SAMRefiner: taming segment anything model for universal mask refinement. arXiv preprint arXiv:2502.06756 (2025)"},{"key":"7_CR25","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"issue":"1","key":"7_CR26","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."},{"issue":"6","key":"7_CR27","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1002\/uog.23646","volume":"58","author":"J Martins","year":"2021","unstructured":"Martins, J., et al.: Influence of maternal body mass index on interobserver variability of fetal ultrasound biometry and amniotic-fluid assessment in late pregnancy. Ultrasound Obstet. Gynecol. 58(6), 892\u2013899 (2021)","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"7_CR28","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"7_CR29","doi-asserted-by":"crossref","unstructured":"Na, S., Guo, Y., Jiang, F., Ma, H., Huang, J.: Segment any cell: a SAM-based auto-prompting fine-tuning framework for nuclei segmentation. arXiv preprint arXiv:2401.13220 (2024)","DOI":"10.1109\/TNNLS.2025.3611322"},{"key":"7_CR30","unstructured":"Ouyang, D., et al.: EchoNet-dynamic: a large new cardiac motion video data resource for medical machine learning. In: NeurIPS ML4H Workshop, Vancouver, BC, Canada, vol.\u00a05 (2019)"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Pan, Y., Niu, L., Yang, X., Niu, Q., Chen, B.: EBTNet: efficient bilateral token mixer network for fetal cardiac ultrasound image segmentation. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3439858"},{"key":"7_CR32","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":"7_CR33","doi-asserted-by":"crossref","unstructured":"Xia, Z., Pan, X., Song, S., Li, L.E., Huang, G.: Vision transformer with deformable attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4794\u20134803 (2022)","DOI":"10.1109\/CVPR52688.2022.00475"},{"key":"7_CR34","unstructured":"Xie, B., Tang, H., Duan, B., Cai, D., Yan, Y.: MaskSAM: towards auto-prompt SAM with mask classification for medical image segmentation. arXiv preprint arXiv:2403.14103 (2024)"},{"key":"7_CR35","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. In: Advances in Neural Information Processing Systems, vol. 34, pp. 12077\u201312090 (2021)"},{"key":"7_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2019.101690","volume":"80","author":"L Xu","year":"2020","unstructured":"Xu, L., et al.: DW-Net: a cascaded convolutional neural network for apical four-chamber view segmentation in fetal echocardiography. Comput. Med. Imaging Graph. 80, 101690 (2020)","journal-title":"Comput. Med. Imaging Graph."},{"key":"7_CR37","doi-asserted-by":"publisher","first-page":"80437","DOI":"10.1109\/ACCESS.2020.2984630","volume":"8","author":"L Xu","year":"2020","unstructured":"Xu, L., Liu, M., Zhang, J., He, Y.: Convolutional-neural-network-based approach for segmentation of apical four-chamber view from fetal echocardiography. IEEE Access 8, 80437\u201380446 (2020)","journal-title":"IEEE Access"},{"issue":"8","key":"7_CR38","doi-asserted-by":"publisher","first-page":"1886","DOI":"10.1109\/TBME.2016.2628401","volume":"64","author":"L Yu","year":"2016","unstructured":"Yu, L., Guo, Y., Wang, Y., Yu, J., Chen, P.: Segmentation of fetal left ventricle in echocardiographic sequences based on dynamic convolutional neural networks. IEEE Trans. Biomed. Eng. 64(8), 1886\u20131895 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"7_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, D.: Customized segment anything model for medical image segmentation. arXiv preprint arXiv:2304.13785 (2023)","DOI":"10.2139\/ssrn.4495221"},{"key":"7_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107386","volume":"107","author":"C Zhao","year":"2021","unstructured":"Zhao, C., et al.: Multi-scale wavelet network algorithm for pediatric echocardiographic segmentation via hierarchical feature guided fusion. Appl. Soft Comput. 107, 107386 (2021)","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"7_CR41","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/JBHI.2023.3328954","volume":"28","author":"L Zhao","year":"2023","unstructured":"Zhao, L., Tan, G., Pu, B., Wu, Q., Ren, H., Li, K.: TransFSM: fetal anatomy segmentation and biometric measurement in ultrasound images using a hybrid transformer. IEEE J. Biomed. Health Inform. 28(1), 285\u2013296 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7_CR42","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable DETR: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020)"},{"key":"7_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xiong, C., Zhao, H., Yao, Y.: SAM-Att: a prompt-free SAM-related model with an attention module for automatic segmentation of the left ventricle in echocardiography. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3384383"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5634-2_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T21:23:12Z","timestamp":1768944192000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5634-2_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556335","9789819556342"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5634-2_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"21 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}