{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T01:08:42Z","timestamp":1784509722745,"version":"3.55.0"},"reference-count":185,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tpami.2024.3452629","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T17:35:11Z","timestamp":1725039311000},"page":"10998-11018","source":"Crossref","is-referenced-by-count":40,"title":["Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0373-3958","authenticated-orcid":false,"given":"Zdravko","family":"Marinov","sequence":"first","affiliation":[{"name":"Department of Informatics, Computer Vision for Human-Computer Interaction Lab, Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6243-2568","authenticated-orcid":false,"given":"Paul F.","family":"J\u00e4ger","sequence":"additional","affiliation":[{"name":"German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-1982","authenticated-orcid":false,"given":"Jan","family":"Egger","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8686-0682","authenticated-orcid":false,"given":"Jens","family":"Kleesiek","sequence":"additional","affiliation":[{"name":"Institute for Artificial Intelligence in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8046-4945","authenticated-orcid":false,"given":"Rainer","family":"Stiefelhagen","sequence":"additional","affiliation":[{"name":"Department of Informatics, Computer Vision for Human-Computer Interaction Lab, Karlsruhe Institute of Technology, Karlsruhe, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2621185"},{"key":"ref2","first-page":"143","article-title":"UI-net: Interactive artificial neural networks for iterative image segmentation based on a user model","volume-title":"Proc. Eurographics Workshop Vis. Comput. Biol. Med.","author":"Amrehn"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67389-9_26"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_27"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2840695"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2791721"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00919-9_42"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11723-8_11"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_47"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.35940\/ijrte.b3825.078219"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_37"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_8"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33642-4_7"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-48004-8"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33642-4_5"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01284"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3364836.3364885"},{"key":"ref18","article-title":"Radiotherapy target contouring with convolutional gated graph neural network","author":"Chao","year":"2019"},{"key":"ref19","article-title":"Interactive user interface based on convolutional auto-encoders for annotating CT-scans","author":"L\u00e4ngkvist","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-35817-4_2"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab6f99"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59719-1_28"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00941"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_45"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3048477"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101771"},{"key":"ref27","article-title":"Interactive deep refinement network for medical image segmentation","author":"Kitrungrotsakul","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1117\/12.2551354"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59861-7_2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14327"},{"key":"ref31","article-title":"Interactive radiotherapy target delineation with 3D-fused context propagation","author":"Chao","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59719-1_56"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/GCCE50665.2020.9291740"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-020-02223-x"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59722-1_52"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3297506"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-02444-3_38"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102102"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3060551"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.3390\/make3020026"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_58"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_52"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_30"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.3390\/app11146279"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.2200299"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3125491"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101998"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i7.16752"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3087735"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.05.121"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1186\/s13326-021-00255-z"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1117\/12.2582241"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9434105"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3082527"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC46164.2021.9629959"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/RIVF51545.2021.9642141"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14852"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636146"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00081"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16120"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15404"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102549"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102334"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16443-9_45"},{"key":"ref65","first-page":"35","article-title":"ECONet: Efficient convolutional online likelihood network for scribble-based interactive segmentation","volume-title":"Proc. Int. Conf. Med. Imag. Deep Learn.","author":"Asad"},{"key":"ref66","first-page":"441","article-title":"i3Deep: Efficient 3D interactive segmentation with the nnU-Net","volume-title":"Proc. Int. Conf. Med. Imag. Deep Learn.","author":"Gotkowski"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17027-0_2"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16440-8_67"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16440-8_64"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-022-00660-5"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100294"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548096"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC48229.2022.9871361"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-17721-7_11"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/BigMM55396.2022.00009"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103723"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.24556"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.3390\/app12031328"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI52829.2022.9761467"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.210214"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102599"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI53787.2023.10230334"},{"key":"ref83","article-title":"Exploring cycle consistency learning in interactive volume segmentation","author":"Liu","year":"2023"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.12.003"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43895-0_53"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43901-8_73"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-022-02730-z"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpi.2022.100160"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2022.12.005"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-023-02931-0"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43898-1_61"},{"key":"ref92","first-page":"36620","article-title":"AbdomenAtlas-8 K: Annotating 8,000 CT volumes for multi-organ segmentation in three weeks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Qu"},{"key":"ref93","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102918"},{"key":"ref94","article-title":"Segment anything model (SAM) for digital pathology: Assess zero-shot segmentation on whole slide imaging","volume-title":"Proc. Int. Conf. Med. Imag. Deep Learn.","author":"Deng"},{"key":"ref95","article-title":"SAM vs BET: A comparative study for brain extraction and segmentation of magnetic resonance images using deep learning","author":"Mohapatra","year":"2023"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1007\/s00066-024-02313-8"},{"key":"ref97","article-title":"When SAM meets medical images: An investigation of segment anything model (SAM) on multi-phase liver tumor segmentation","author":"Hu","year":"2023"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/iccvw60793.2023.00361"},{"key":"ref99","article-title":"Medical SAM adapter: Adapting segment anything model for medical image segmentation","author":"Wu","year":"2023"},{"key":"ref100","article-title":"Learnable ophthalmology SAM","author":"Qiu","year":"2023"},{"key":"ref101","article-title":"Accuracy of segment-anything model (SAM) in medical image segmentation tasks","author":"He","year":"2023"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13111947"},{"key":"ref103","article-title":"GazeSAM: What you see is what you segment","author":"Wang","year":"2023"},{"key":"ref104","article-title":"SkinSAM: Empowering skin cancer segmentation with segment anything model","author":"Hu","year":"2023"},{"key":"ref105","first-page":"234","article-title":"SAM meets robotic surgery: An empirical study in robustness perspective","volume-title":"Proc. Int. Conf. Med. Image Comput. Comput.- Assist. Interv. Workshops","author":"Wang"},{"key":"ref106","article-title":"SAM on medical images: A. comprehensive study on three prompt modes","author":"Cheng","year":"2023"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/bibe60311.2023.00025"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1117\/12.3006809"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52733.2024.01074"},{"key":"ref110","article-title":"BreastSAM: A study of segment anything model for breast tumor detection in ultrasound images","author":"Hu","year":"2023"},{"key":"ref111","doi-asserted-by":"crossref","DOI":"10.1101\/2023.05.25.542052","article-title":"IAMSAM: Image-based analysis of molecular signatures using the segment-anything model","author":"Lee","year":"2023"},{"key":"ref112","article-title":"DeSAM: Decoupling segment anything model for generalizable medical image segmentation","author":"Gao","year":"2023"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM58861.2023.10385291"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.5582\/bst.2023.01119"},{"key":"ref115","article-title":"Segment anything model (SAM) for radiation oncology","author":"Zhang","year":"2023"},{"key":"ref116","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103370"},{"key":"ref117","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47425-5_33"},{"key":"ref118","article-title":"3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable medical image segmentation","author":"Gong","year":"2023"},{"key":"ref119","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103061"},{"key":"ref120","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"ref121","article-title":"SAM.MD: Zero-shot medical image segmentation capabilities of the segment anything model","volume-title":"Proc. Int. Conf. Med. Imag. Deep Learn.","author":"Roy"},{"key":"ref122","doi-asserted-by":"publisher","DOI":"10.1145\/1924559.1924596"},{"key":"ref123","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1986.4767851"},{"key":"ref124","doi-asserted-by":"publisher","DOI":"10.1109\/3477.658584"},{"key":"ref125","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2161484"},{"key":"ref126","doi-asserted-by":"publisher","DOI":"10.1006\/jvci.2001.0500"},{"key":"ref127","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref128","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref129","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref130","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref131","first-page":"105","article-title":"Interactive graph cuts for optimal boundary & region segmentation of objects in ND images","volume-title":"Proc. IEEE Int. Conf. Comput. Vis.","author":"Boykov"},{"key":"ref132","doi-asserted-by":"publisher","DOI":"10.1109\/83.841934"},{"key":"ref133","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.120"},{"key":"ref134","article-title":"BET2: MR-based estimation of brain, skull and scalp surfaces","volume-title":"Proc. 11th Annu. Meeting Org. Hum. Brain Mapping","author":"Jenkinson"},{"key":"ref135","first-page":"272","article-title":"Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images","volume-title":"Proc. Int. MICCAI Brainlesion Workshop","author":"Hatamizadeh"},{"key":"ref136","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref137","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref138","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Dosovitskiy"},{"key":"ref139","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-151-4-200908180-00135"},{"key":"ref140","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.47"},{"key":"ref141","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015720"},{"key":"ref142","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.85"},{"key":"ref143","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref144","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126343"},{"key":"ref145","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.03.008"},{"key":"ref146","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01911"},{"key":"ref147","doi-asserted-by":"publisher","DOI":"10.1016\/S0166-4115(08)62386-9"},{"key":"ref148","doi-asserted-by":"publisher","DOI":"10.5948\/upo9781614440260.011"},{"key":"ref149","article-title":"MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images","author":"Diaz-Pinto","year":"2022"},{"key":"ref150","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00232-0"},{"key":"ref151","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19303-w"},{"key":"ref152","article-title":"Iteratively trained interactive segmentation","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Mahadevan"},{"key":"ref153","doi-asserted-by":"publisher","DOI":"10.1007\/BF00133570"},{"key":"ref154","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-2572595\/v1","article-title":"The autoPET challenge: Towards fully automated lesion segmentation in oncologic PET\/CT imaging","author":"Gatidis","year":"2023"},{"issue":"7","key":"ref155","first-page":"1","article-title":"An overview of interactive medical image segmentation","volume":"2013","author":"Zhao","year":"2013","journal-title":"Ann. BMVA"},{"key":"ref156","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(00)00041-4"},{"key":"ref157","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-020-0177-5"},{"key":"ref158","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11479-4_9"},{"key":"ref159","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01476-x"},{"key":"ref160","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"ref161","doi-asserted-by":"publisher","DOI":"10.3390\/mti2030047"},{"key":"ref162","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2377694"},{"key":"ref163","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30695-9"},{"key":"ref164","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680"},{"key":"ref165","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-02151-z"},{"key":"ref166","first-page":"109","article-title":"Efficient inference in fully connected CRFs with Gaussian edge potentials","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kr\u00e4henb\u00fchl"},{"key":"ref167","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39899-8_70"},{"key":"ref168","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2011.5872394"},{"key":"ref169","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"ref170","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-023-02150-0"},{"key":"ref171","doi-asserted-by":"publisher","DOI":"10.2196\/26151"},{"key":"ref172","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106534"},{"key":"ref173","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2005.04.005"},{"key":"ref174","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107398"},{"key":"ref175","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.477"},{"key":"ref176","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00071"},{"key":"ref177","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00865"},{"key":"ref178","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00544"},{"key":"ref179","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00067"},{"key":"ref180","doi-asserted-by":"publisher","DOI":"10.1109\/ICE2T.2017.8215985"},{"key":"ref181","article-title":"Polyp-SAM: Can a text guided sam perform better for polyp segmentation?","author":"Biswas","year":"2023"},{"key":"ref182","article-title":"Lung grounded-SAM (LuGSAM): A novel framework for integrating text prompts to segment anything model (SAM) for segmentation tasks of ICU chest X-Rays","author":"Ramesh","year":"2023"},{"key":"ref183","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01934"},{"key":"ref184","article-title":"One model to rule them all: Towards universal segmentation for medical images with text prompts","author":"Zhao","year":"2023"},{"key":"ref185","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72390-2_60"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10746266\/10660300.pdf?arnumber=10660300","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:32:56Z","timestamp":1732667576000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10660300\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":185,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3452629","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}