{"id":"https://openalex.org/W7131085809","doi":"https://doi.org/10.48550/arxiv.2602.17813","title":"Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation","display_name":"Promptable segmentation with region exploration enables minimal-effort expert-level prostate cancer delineation","publication_year":2026,"publication_date":"2026-02-19","ids":{"openalex":"https://openalex.org/W7131085809","doi":"https://doi.org/10.48550/arxiv.2602.17813"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.17813","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101169048","display_name":"Junqing Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yang, Junqing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126604710","display_name":"Natasha Thorley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thorley, Natasha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089153224","display_name":"Ahmed Nadeem Abbas\u0131","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abbasi, Ahmed Nadeem","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126645009","display_name":"Shonit Punwani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Punwani, Shonit","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126605092","display_name":"Zion Tse","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tse, Zion","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126605600","display_name":"Yipeng Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yipeng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126596408","display_name":"Shaheer U. Saeed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saeed, Shaheer U.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101169048"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.8285999894142151,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10124","display_name":"Prostate Cancer Diagnosis and Treatment","score":0.8285999894142151,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.05979999899864197,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.04089999943971634,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8271999955177307},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5210000276565552},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5098000168800354},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45829999446868896},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.421099990606308},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.38089999556541443},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.3589000105857849},{"id":"https://openalex.org/keywords/prostate-cancer","display_name":"Prostate cancer","score":0.3384000062942505}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8271999955177307},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7105000019073486},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6172000169754028},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5210000276565552},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5098000168800354},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45829999446868896},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.421099990606308},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3950999975204468},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3831000030040741},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.38089999556541443},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C2780192828","wikidata":"https://www.wikidata.org/wiki/Q181257","display_name":"Prostate cancer","level":3,"score":0.3384000062942505},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.33410000801086426},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3206000030040741},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3124000132083893},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2946000099182129},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2858000099658966},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.2721000015735626},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C206824153","wikidata":"https://www.wikidata.org/wiki/Q1169834","display_name":"Region growing","level":5,"score":0.25540000200271606},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.17813","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.17813","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.17813","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.17813","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.6206427812576294,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Purpose:":[0],"Accurate":[1],"segmentation":[2,75,86,101,135,151,184],"of":[3,158],"prostate":[4,206],"cancer":[5],"on":[6,51,203],"magnetic":[7],"resonance":[8],"(MR)":[9],"images":[10],"is":[11,119],"crucial":[12],"for":[13],"planning":[14],"image-guided":[15],"interventions":[16],"such":[17],"as":[18],"targeted":[19],"biopsies,":[20],"cryoablation,":[21],"and":[22,26,34,73,133,153,168,181,210,214,226],"radiotherapy.":[23],"However,":[24],"subtle":[25],"variable":[27],"tumour":[28],"appearances,":[29],"differences":[30],"in":[31],"imaging":[32],"protocols,":[33],"limited":[35],"expert":[36],"availability":[37],"make":[38],"consistent":[39],"interpretation":[40],"difficult.":[41],"While":[42],"automated":[43,72,183,196,222],"methods":[44,223],"aim":[45],"to":[46,67,136,164,232],"address":[47],"this,":[48],"they":[49],"rely":[50],"large":[52],"expertly-annotated":[53],"datasets":[54,208],"that":[55],"are":[56],"often":[57],"inconsistent,":[58],"whereas":[59],"manual":[60,74,180,233],"delineation":[61],"remains":[62],"labour-intensive.":[63],"This":[64],"work":[65],"aims":[66],"bridge":[68],"the":[69,127,131,146,162,177,219],"gap":[70],"between":[71],"through":[76,122],"a":[77,99,115,138],"framework":[78,93,178,200],"driven":[79],"by":[80,104,187,224],"user-provided":[81],"point":[82,111],"prompts,":[83],"enabling":[84],"accurate":[85],"with":[87,98,212,229],"minimal":[88],"annotation":[89,237],"effort.":[90],"Methods:":[91],"The":[92,199],"combines":[94],"reinforcement":[95],"learning":[96],"(RL)":[97],"region-growing":[100,113],"process":[102],"guided":[103],"user":[105,190],"prompts.":[106],"Starting":[107],"from":[108,141],"an":[109],"initial":[110],"prompt,":[112],"generates":[114],"preliminary":[116],"segmentation,":[117,235],"which":[118,142],"iteratively":[120],"refined":[121],"RL.":[123],"At":[124],"each":[125],"step,":[126],"RL":[128],"agent":[129,163],"observes":[130],"image":[132],"current":[134,194],"predict":[137],"new":[139],"point,":[140],"region":[143],"growing":[144],"updates":[145],"mask.":[147],"A":[148],"reward,":[149],"balancing":[150],"accuracy":[152],"voxel-wise":[154],"uncertainty,":[155],"encourages":[156],"exploration":[157],"ambiguous":[159],"regions,":[160],"allowing":[161],"escape":[165],"local":[166],"optima":[167],"perform":[169],"sample-specific":[170],"optimisation.":[171],"Despite":[172],"requiring":[173],"fully":[174,182,195],"supervised":[175],"training,":[176],"bridges":[179],"at":[185],"inference":[186],"substantially":[188],"reducing":[189,236],"effort":[191],"while":[192],"outperforming":[193],"methods.":[197],"Results:":[198],"was":[201],"evaluated":[202],"two":[204],"public":[205],"MR":[207],"(PROMIS":[209],"PICAI,":[211],"566":[213],"1090":[215],"cases).":[216],"It":[217],"outperformed":[218],"previous":[220],"best":[221],"9.9%":[225],"8.9%,":[227],"respectively,":[228],"performance":[230],"comparable":[231],"radiologist":[234],"time":[238],"tenfold.":[239]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-24T00:00:00"}