{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:18:58Z","timestamp":1783117138523,"version":"3.54.6"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62576153"],"award-info":[{"award-number":["62576153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476054"],"award-info":[{"award-number":["62476054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.patcog.2026.113852","type":"journal-article","created":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T14:38:43Z","timestamp":1777214323000},"page":"113852","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["Structured prompt-guided knowledge injection for medical image segmentation"],"prefix":"10.1016","volume":"179","author":[{"given":"Yiyang","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelei","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.113852_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109228","article-title":"An effective CNN and transformer complementary network for medical image segmentation","volume":"136","author":"Yuan","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113852_b2","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.patcog.2026.113852_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110491","article-title":"UCTNet: Uncertainty-guided CNN-transformer hybrid networks for medical image segmentation","volume":"152","author":"Guo","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.113852_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-net architecture design for medical image segmentation through the lens of transformers","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.113852_b5","series-title":"Proc. Eur. Conf. Comput. Vis.","first-page":"205","article-title":"Swin-unet: Unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"10.1016\/j.patcog.2026.113852_b6","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1109\/TIP.2023.3293771","article-title":"Nnformer: Volumetric medical image segmentation via a 3D transformer","volume":"32","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Imag. Process."},{"issue":"4","key":"10.1016\/j.patcog.2026.113852_b7","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.1109\/TCSVT.2023.3312738","article-title":"Mcl: multimodal contrastive learning for deepfake detection","volume":"34","author":"Liu","year":"2023","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113852_b8","first-page":"3876","article-title":"Medclip: Contrastive learning from unpaired medical images and text","volume":"vol. 2022","author":"Wang","year":"2022"},{"key":"10.1016\/j.patcog.2026.113852_b9","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.patcog.2026.113852_b10","series-title":"Deep Learning in Med. Image Anal. and Multimodal Learning for Clinical Decision Support: 4th International Workshop, MICCAI","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"},{"key":"10.1016\/j.patcog.2026.113852_b11","doi-asserted-by":"crossref","unstructured":"L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, H. Adam, Encoder-decoder with atrous separable convolution for semantic image segmentation, in: Proc. Eur. Conf. Comput. Vis., 2018, pp. 801\u2013818.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10.1016\/j.patcog.2026.113852_b12","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2021","journal-title":"Proc. Int. Conf. Learn. Represent."},{"key":"10.1016\/j.patcog.2026.113852_b13","doi-asserted-by":"crossref","unstructured":"Z. Liu, et al., Swin transformer: Hierarchical vision transformer using shifted windows, in: Proc. Int. Conf. Comput. Vis., 2021, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"9","key":"10.1016\/j.patcog.2026.113852_b14","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TMI.2024.3398728","article-title":"UNETR++: delving into efficient and accurate 3D medical image segmentation","volume":"43","author":"Shaker","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.patcog.2026.113852_b15","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/TMI.2024.3417007","article-title":"PolarFormer: A transformer-based method for multi-lesion segmentation in intravascular OCT","volume":"43","author":"Huang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113852_b16","series-title":"Int. Conf. Mach. Learn.","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","author":"Radford","year":"2021"},{"key":"10.1016\/j.patcog.2026.113852_b17","series-title":"Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","author":"Zhang","year":"2023"},{"key":"10.1016\/j.patcog.2026.113852_b18","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"643","article-title":"Medclip-sam: Bridging text and image towards universal medical image segmentation","author":"Koleilat","year":"2024"},{"issue":"1","key":"10.1016\/j.patcog.2026.113852_b19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/TMI.2023.3291719","article-title":"Lvit: language meets vision transformer in medical image segmentation","volume":"43","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113852_b20","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"634","article-title":"SimTxtSeg: Weakly-supervised medical image segmentation with simple text cues","author":"Xie","year":"2024"},{"key":"10.1016\/j.patcog.2026.113852_b21","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"253","article-title":"Enhancing label-efficient medical image segmentation with text-guided diffusion models","author":"Feng","year":"2024"},{"key":"10.1016\/j.patcog.2026.113852_b22","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"77","article-title":"Causalclipseg: Unlocking clip\u2019s potential in referring medical image segmentation with causal intervention","author":"Chen","year":"2024"},{"issue":"1","key":"10.1016\/j.patcog.2026.113852_b23","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1006\/cviu.1995.1004","article-title":"Active shape models-their training and application","volume":"61","author":"Cootes","year":"1995","journal-title":"Comput. Vis. Image Underst."},{"issue":"6","key":"10.1016\/j.patcog.2026.113852_b24","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/34.927467","article-title":"Active appearance models","volume":"23","author":"Cootes","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2026.113852_b25","doi-asserted-by":"crossref","unstructured":"W. Huang, J. Hu, X. Bi, B. Xiao, Anatomical prior guided spatial contrastive learning for few-shot medical image segmentation, in: ACMMM, 2024, pp. 5211\u20135220.","DOI":"10.1145\/3664647.3680558"},{"key":"10.1016\/j.patcog.2026.113852_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2024.102442","article-title":"Prior knowledge-guided vision-transformer-based unsupervised domain adaptation for intubation prediction in lung disease at one week","volume":"118","author":"Yang","year":"2024","journal-title":"Comput. Med. Imag. Grap."},{"key":"10.1016\/j.patcog.2026.113852_b27","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"238","article-title":"Knowledge-guided prompt learning for lifespan brain MR image segmentation","author":"Teng","year":"2024"},{"key":"10.1016\/j.patcog.2026.113852_b28","doi-asserted-by":"crossref","unstructured":"J. Wu, M. Xu, One-prompt to segment all medical images, in: Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit., 2024, pp. 11302\u201311312.","DOI":"10.1109\/CVPR52733.2024.01074"},{"key":"10.1016\/j.patcog.2026.113852_b29","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"487","article-title":"Curriculum prompting foundation models for medical image segmentation","author":"Zheng","year":"2024"},{"key":"10.1016\/j.patcog.2026.113852_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103222","article-title":"Anatomically plausible segmentations: Explicitly preserving topology through prior deformations","volume":"97","author":"Wyburd","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.113852_b31","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"604","article-title":"Striving for simplicity: Simple yet effective prior-aware pseudo-labeling for semi-supervised ultrasound image segmentation","author":"Chen","year":"2024"},{"issue":"2","key":"10.1016\/j.patcog.2026.113852_b32","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1109\/TMI.2024.3476176","article-title":"Glandsam: Injecting morphology knowledge into segment anything model for label-free gland segmentation","volume":"44","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.patcog.2026.113852_b33","series-title":"Hello GPT-4o","author":"OpenAI","year":"2024"},{"key":"10.1016\/j.patcog.2026.113852_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106389","article-title":"Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules","volume":"155","author":"Gong","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.patcog.2026.113852_b35","first-page":"346","article-title":"Comparison of thyroid segmentation techniques for 3D ultrasound","volume":"vol. 10133","author":"Wunderling","year":"2017"},{"issue":"7","key":"10.1016\/j.patcog.2026.113852_b36","doi-asserted-by":"crossref","first-page":"7020","DOI":"10.1109\/TCSVT.2024.3514181","article-title":"Frequency-aware interaction network for ultrasound image segmentation","volume":"35","author":"Wang","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113852_b37","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.compbiomed.2019.05.002","article-title":"Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm","volume":"109","author":"Buda","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.patcog.2026.113852_b38","series-title":"MMM","first-page":"451","article-title":"Kvasir-seg: A segmented polyp dataset","author":"Jha","year":"2020"},{"key":"10.1016\/j.patcog.2026.113852_b39","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","article-title":"WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians","volume":"43","author":"Bernal","year":"2015","journal-title":"Comput. Med. Imag. Grap."},{"issue":"1","key":"10.1016\/j.patcog.2026.113852_b40","article-title":"A benchmark for endoluminal scene segmentation of colonoscopy images","volume":"2017","author":"V\u00e1zquez","year":"2017","journal-title":"J. Heal. Eng."},{"key":"10.1016\/j.patcog.2026.113852_b41","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","article-title":"Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer","volume":"9","author":"Silva","year":"2014","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.patcog.2026.113852_b42","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"263","article-title":"Pranet: Parallel reverse attention network for polyp segmentation","author":"Fan","year":"2020"},{"key":"10.1016\/j.patcog.2026.113852_b43","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"14","article-title":"Transfuse: Fusing transformers and cnns for medical image segmentation","author":"Zhang","year":"2021"},{"key":"10.1016\/j.patcog.2026.113852_b44","doi-asserted-by":"crossref","unstructured":"M. Heidari, et al., Hiformer: Hierarchical multi-scale representations using transformers for medical image segmentation, in: Proc. Int. Conf. Workshop on Applications of Computer Vision, 2023, pp. 6202\u20136212.","DOI":"10.1109\/WACV56688.2023.00614"},{"key":"10.1016\/j.patcog.2026.113852_b45","series-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent.","first-page":"416","article-title":"Swinunetr-v2: Stronger swin transformers with stagewise convolutions for 3d medical image segmentation","author":"He","year":"2023"},{"issue":"8","key":"10.1016\/j.patcog.2026.113852_b46","doi-asserted-by":"crossref","first-page":"7440","DOI":"10.1109\/TCSVT.2024.3370685","article-title":"Uncertainty-aware hierarchical aggregation network for medical image segmentation","volume":"34","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113852_b47","first-page":"4652","article-title":"U-kan makes strong backbone for medical image segmentation and generation","volume":"vol. 39","author":"Li","year":"2025"},{"key":"10.1016\/j.patcog.2026.113852_b48","first-page":"4571","article-title":"ConDSeg: A general medical image segmentation framework via contrast-driven feature enhancement","volume":"vol. 39","author":"Lei","year":"2025"},{"key":"10.1016\/j.patcog.2026.113852_b49","series-title":"MICCAI","first-page":"151","article-title":"Tganet: Text-guided attention for improved polyp segmentation","author":"Tomar","year":"2022"},{"issue":"4","key":"10.1016\/j.patcog.2026.113852_b50","doi-asserted-by":"crossref","first-page":"3234","DOI":"10.1109\/TCSVT.2024.3508752","article-title":"Cmirnet: Cross-modal interactive reasoning network for referring image segmentation","volume":"35","author":"Xu","year":"2025","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"issue":"2","key":"10.1016\/j.patcog.2026.113852_b51","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"Nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.patcog.2026.113852_b52","article-title":"Polyp-PVT: Polyp segmentation with pyramid vision transformers","volume":"2","author":"Dong","year":"2023","journal-title":"CAAI Artif. Intell. Res."},{"key":"10.1016\/j.patcog.2026.113852_b53","series-title":"MICCAI","first-page":"724","article-title":"Ariadne\u2019s thread: Using text prompts to improve segmentation of infected areas from chest x-ray images","author":"Zhong","year":"2023"},{"issue":"9","key":"10.1016\/j.patcog.2026.113852_b54","doi-asserted-by":"crossref","first-page":"5040","DOI":"10.1109\/TCYB.2024.3368154","article-title":"CTNet: Contrastive transformer network for polyp segmentation","volume":"54","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Cybern."},{"issue":"12","key":"10.1016\/j.patcog.2026.113852_b55","doi-asserted-by":"crossref","first-page":"12594","DOI":"10.1109\/TCSVT.2024.3432882","article-title":"Polyp segmentation via semantic enhanced perceptual network","volume":"34","author":"Wang","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"10.1016\/j.patcog.2026.113852_b56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3470962","article-title":"Dual-domain feature interaction network for automatic colorectal polyp segmentation","volume":"73","author":"Yue","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.patcog.2026.113852_b57","series-title":"MICCAI","first-page":"425","article-title":"Madapter: A better interaction between image and language for medical image segmentation","author":"Zhang","year":"2024"},{"issue":"1","key":"10.1016\/j.patcog.2026.113852_b58","doi-asserted-by":"crossref","DOI":"10.1056\/AIoa2400640","article-title":"A multimodal biomedical foundation model trained from fifteen million image\u2013text pairs","volume":"2","author":"Zhang","year":"2025","journal-title":"NEJM AI"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326008174?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326008174?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T21:40:24Z","timestamp":1783114824000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326008174"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":58,"alternative-id":["S0031320326008174"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113852","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Structured prompt-guided knowledge injection for medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.113852","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113852"}}