{"id":"https://openalex.org/W6903339313","doi":"https://doi.org/10.11588/heidok.00029345","title":"From Manual to Automated Design of Biomedical Semantic Segmentation Methods","display_name":"From Manual to Automated Design of Biomedical Semantic Segmentation Methods","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W6903339313","doi":"https://doi.org/10.11588/heidok.00029345"},"language":"en","primary_location":{"id":"pmh:oai:archiv.ub.uni-heidelberg.de:29345","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402333","display_name":"heiDOK (Heidelberg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I223822909","host_organization_name":"Heidelberg University","host_organization_lineage":["https://openalex.org/I223822909"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"type":"dissertation","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":null,"display_name":"Isensee, Fabian","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Isensee, Fabian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"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":true,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7702000141143799,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7702000141143799,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.041200000792741776,"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"}},{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.030899999663233757,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.8695999979972839},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.6187999844551086},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.5842999815940857},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.532800018787384},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3961000144481659},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.3610999882221222},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3472999930381775},{"id":"https://openalex.org/keywords/diversity","display_name":"Diversity (politics)","score":0.32519999146461487},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30489999055862427}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8695999979972839},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7455999851226807},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.6187999844551086},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5979999899864197},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.5842999815940857},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.532800018787384},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3492000102996826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.32519999146461487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31859999895095825},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C42781572","wikidata":"https://www.wikidata.org/wiki/Q1250322","display_name":"Digital image","level":4,"score":0.28519999980926514},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2554999887943268},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.24899999797344208},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.23600000143051147},{"id":"https://openalex.org/C206824153","wikidata":"https://www.wikidata.org/wiki/Q1169834","display_name":"Region growing","level":5,"score":0.23119999468326569},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.23070000112056732},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2305999994277954},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2273000031709671},{"id":"https://openalex.org/C2777946921","wikidata":"https://www.wikidata.org/wiki/Q7449044","display_name":"Semantic analysis (machine learning)","level":2,"score":0.22050000727176666},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2143000066280365},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.21209999918937683},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2062000036239624},{"id":"https://openalex.org/C42314347","wikidata":"https://www.wikidata.org/wiki/Q6865488","display_name":"Minimum spanning tree-based segmentation","level":5,"score":0.20329999923706055},{"id":"https://openalex.org/C55020928","wikidata":"https://www.wikidata.org/wiki/Q3813865","display_name":"Image quality","level":3,"score":0.1973000019788742},{"id":"https://openalex.org/C2909954168","wikidata":"https://www.wikidata.org/wiki/Q860755","display_name":"Digital image analysis","level":2,"score":0.19589999318122864},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.19349999725818634},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.19099999964237213},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.18799999356269836},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.18410000205039978},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.17739999294281006},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.17479999363422394},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.17440000176429749},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.16670000553131104}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:archiv.ub.uni-heidelberg.de:29345","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402333","display_name":"heiDOK (Heidelberg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I223822909","host_organization_name":"Heidelberg University","host_organization_lineage":["https://openalex.org/I223822909"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},{"id":"doi:10.11588/heidok.00029345","is_oa":true,"landing_page_url":"https://doi.org/10.11588/heidok.00029345","pdf_url":null,"source":{"id":"https://openalex.org/S7407051545","display_name":"University Library Heidelberg","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:archiv.ub.uni-heidelberg.de:29345","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306402333","display_name":"heiDOK (Heidelberg University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I223822909","host_organization_name":"Heidelberg University","host_organization_lineage":["https://openalex.org/I223822909"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Digital":[0],"imaging":[1],"plays":[2,64],"an":[3,108,443],"increasingly":[4],"important":[5],"role":[6,67],"in":[7,68,96,161,198,218,276,294,300,306,312,352,378,463,635,653,663,744,770,777,782,863],"clinical":[8],"practice.":[9],"With":[10],"the":[11,20,22,48,51,58,90,101,119,221,234,253,277,291,297,303,309,313,321,349,353,387,397,403,421,426,509,560,564,596,604,611,615,636,651,657,684,692,731,737,745,771,805,864],"number":[12,23,111,584],"of":[13,24,57,60,82,89,94,103,112,127,137,190,223,233,252,271,279,320,332,399,429,483,511,553,585,598,617,650,656,680,691,728,748,755,767,804,866],"images":[14],"that":[15,177,326,371,389,406,424,488,523,567,670,688,821,839],"are":[16,122,178,238,547,785,793],"routinely":[17],"acquired":[18],"on":[19,50,180,355,415,678],"rise,":[21],"experts":[25,52],"devoted":[26],"to":[27,46,149,152,185,203,241,380,386,408,494,527,627,698,721,764,787,795,802,824,830,845],"analyzing":[28],"them":[29,147],"is":[30,105,201,274,346,489,498,502,568,588,610,667,683,815],"by":[31,77,107,439,504,761,869],"far":[32],"not":[33,130,525,848],"increasing":[34],"as":[35,80],"rapidly.":[36],"This":[37,384,459,501,666],"alarming":[38],"disparity":[39],"calls":[40],"for":[41,118,257,330,413,455,531,540,576,733,808,854],"automated":[42],"image":[43,98],"analysis":[44,319,597],"methods":[45,128,140,237,273,324,435,513,696,757,868],"ease":[47],"burden":[49],"and":[53,86,192,206,231,264,266,308,369,402,491,530,563,716,776,789,841,872],"prevent":[54],"a":[55,65,142,188,194,227,339,393,400,409,451,456,464,484,533,550,582,647,701,725,765,779,816,836],"degradation":[56],"quality":[59],"care.":[61],"Semantic":[62],"segmentation":[63,113,139,154,216,229,244,255,263,323,328,367,411,431,434,453,512,561,633,660,695,749,756,807,837,852,867],"central":[66],"extracting":[69],"clinically":[70],"relevant":[71],"information":[72],"from":[73,211,632],"images,":[74],"either":[75],"all":[76,331,541],"themselves":[78],"or":[79],"part":[81],"more":[83,859],"elaborate":[84],"pipelines,":[85,603],"constitutes":[87],"one":[88,181],"most":[91],"active":[92],"fields":[93],"research":[95],"medical":[97],"analysis.":[99],"Thereby,":[100],"diversity":[102,126],"datasets":[104,120,542,630,729,768,811],"mirrored":[106],"equally":[109],"diverse":[110,629],"methods,":[114],"each":[115,495,577,679],"being":[116,640],"optimized":[117],"they":[121,448],"addressing.":[123],"The":[124,134,752],"resulting":[125],"does":[129,158],"come":[131],"without":[132,642,730],"downsides:":[133],"specialized":[135,602,674],"nature":[136,720],"these":[138,333,664],"causes":[141],"dataset":[143,182,382,401,427,496,578,753],"dependency":[144,428,754],"which":[145,199,232,345,468,532,556,783],"makes":[146,226,481,689],"unable":[148],"be":[150,336,437,470,528,538,574,712,722,825],"transferred":[151],"other":[153,287,473],"problems.":[155],"Not":[156],"only":[157,849],"this":[159,247,416,832],"result":[160],"issues":[162],"with":[163,220,286,338,472,549,571,834],"out-of-the-box":[164,686,851],"applicability,":[165],"but":[166,341,856],"it":[167,200,497,572,703],"also":[168,857],"adversely":[169],"affects":[170],"future":[171,705],"method":[172,423,486,706,750,813,838],"development:":[173,707],"Improvements":[174],"over":[175],"baselines":[176],"demonstrated":[179],"rarely":[183],"transfer":[184],"another,":[186],"testifying":[187],"lack":[189],"reproducibility":[191],"causing":[193,773],"frustrating":[195],"literature":[196,354,780],"landscape":[197,781],"difficult":[202,786,794],"discern":[204],"veritable":[205],"long":[207],"lasting":[208],"methodological":[209,791],"advances":[210,792],"noise.&#13;\\n&#13;\\nWe":[212],"study":[213],"three":[214],"different":[215,659],"tasks":[217,334,661],"depth":[219],"goal":[222],"understanding":[224],"what":[225],"good":[228,410,452,534],"model":[230,412],"recently":[235],"proposed":[236],"truly":[239],"required":[240],"obtain":[242],"competitive":[243,315,327],"performance.":[245],"To":[246],"end,":[248],"we":[249,361,390,418,518],"design":[250,404,510,586,709],"state":[251,649,690,803],"art":[254,652,693,806],"models":[256],"brain":[258],"tumor":[259,268],"segmentation,":[260],"cardiac":[261],"substructure":[262],"kidney":[265,267],"segmentation.":[269],"Each":[270],"our":[272,366,599,618],"evaluated":[275,723],"context":[278],"international":[280],"competitions,":[281],"ensuring":[282],"objective":[283],"performance":[284,329],"comparison":[285],"methods.":[288,432],"We":[289,624,828],"obtained":[290],"third":[292],"place":[293,299,305,311],"BraTS":[295,301],"2017,":[296],"second":[298],"2018,":[302],"first":[304,310,422,685],"ACDC":[307],"highly":[314],"KiTS":[316],"challenge.":[317],"Our":[318],"four":[322],"reveals":[325],"can":[335,391,537,711],"achieved":[337,503],"standard,":[340],"well-tuned":[342],"U-Net":[343],"architecture,":[344],"surprising":[347],"given":[348,457],"recent":[350],"focus":[351],"finding":[356],"better":[357],"network":[358,565,606],"architectures.":[359],"Furthermore,":[360],"identify":[362,392,519],"certain":[363,381,520],"similarities":[364],"between":[365,396],"pipelines":[368],"notice":[370],"their":[372,809],"dissimilarities":[373],"merely":[374],"reflect":[375],"well-structured":[376],"adaptations":[377],"response":[379],"properties.":[383],"leads":[385],"hypothesis":[388,417],"direct":[394],"relation":[395],"properties":[398],"choices":[405,587],"lead":[407],"it.&#13;\\n&#13;\\nBased":[414],"develop":[419],"nnU-Net,":[420,835],"breaks":[425],"traditional":[430],"Traditional":[433],"must":[436,573],"developed":[438],"experts,":[440],"going":[441],"through":[442,590],"iterative":[444],"trial-and-error":[445,819],"process":[446,460,820],"until":[447],"have":[449],"identified":[450],"pipeline":[454,466,521,562],"dataset.":[458],"ultimately":[461],"results":[462,784],"fixed":[465],"configuration":[467],"may":[469],"incompatible":[471],"datasets,":[474,847],"requiring":[475],"extensive":[476],"re-optimization.":[477],"In":[478],"contrast,":[479],"nnU-Net":[480,620,626,645,671,682,715],"use":[482],"generalizing":[485],"template":[487],"dynamically":[490,842],"automatically":[492,840],"adapted":[493,529,575],"applied":[499,641],"to.":[500],"condensing":[505],"domain":[506],"knowledge":[507],"about":[508],"into":[514,714],"inductive":[515],"biases.":[516],"Specifically,":[517],"hyperparameters":[522],"do":[524],"need":[526,732],"default":[535],"value":[536],"set":[539,552],"(called":[543],"blueprint":[544],"parameters).":[545,580,594],"They":[546],"complemented":[548],"comprehensible":[551],"heuristic":[554],"rules,":[555],"explicitly":[557],"encode":[558],"how":[559],"architecture":[566,607],"used":[569,609],"along":[570],"(inferred":[579],"Finally,":[581],"limited":[583],"determined":[589],"empirical":[591],"evaluation":[592,775,874],"(empirical":[593],"Following":[595],"previously":[600],"designed":[601],"basic":[605],"type":[608],"standard":[612],"U-Net,":[613],"coining":[614],"name":[616],"method:":[619],"(\u201dNo":[621],"New":[622],"Net\u201d).":[623],"apply":[625],"19":[628],"originating":[631],"competitions":[634],"biomedical":[637],"domain.":[638],"Despite":[639],"manual":[643,734],"intervention,":[644],"sets":[646],"new":[648,708],"29":[654],"out":[655],"49":[658],"encountered":[662],"datasets.":[665,877],"remarkable":[668],"considering":[669],"competed":[672],"against":[673],"manually":[675],"tuned":[676],"algorithms":[677],"them.":[681],"tool":[687],"semantic":[694],"accessible":[697],"non-experts.":[699],"As":[700],"framework,":[702],"catalyzes":[704],"concepts":[710],"implemented":[713],"leverage":[717],"its":[718],"dynamic":[719],"across":[724,875],"wide":[726],"variety":[727],"re-tuning.&#13;\\n&#13;\\nIn":[735],"conclusion,":[736],"thesis":[738],"presented":[739],"here":[740],"exposed":[741],"critical":[742],"weaknesses":[743],"current":[746],"way":[747],"development.":[751],"impedes":[758],"scientific":[759],"progress":[760],"confining":[762],"researchers":[763],"subset":[766],"available":[769,853],"domain,":[772],"noisy":[774],"turn":[778],"reproduce":[788],"true":[790],"discern.":[796],"Additionally,":[797],"non-experts":[798],"were":[799],"barred":[800],"access":[801],"custom":[810],"because":[812],"development":[814,865],"time":[817],"consuming":[818],"needs":[822],"expertise":[823],"done":[826],"correctly.":[827],"propose":[829],"address":[831],"situation":[833],"adapts":[843],"itself":[844],"arbitrary":[846],"making":[850,862],"everyone":[855],"enabling":[858,870],"robust":[860],"decision":[861],"easy":[871],"convenient":[873],"multiple":[876]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
