{"id":"https://openalex.org/W4362663982","doi":"https://doi.org/10.1117/12.2651992","title":"Catheter segmentation in chest x-ray: improving imbalanced segmentation with a class frequency weighted loss function","display_name":"Catheter segmentation in chest x-ray: improving imbalanced segmentation with a class frequency weighted loss function","publication_year":2023,"publication_date":"2023-04-06","ids":{"openalex":"https://openalex.org/W4362663982","doi":"https://doi.org/10.1117/12.2651992"},"language":"en","primary_location":{"id":"doi:10.1117/12.2651992","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2651992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Computer-Aided Diagnosis","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5044881983","display_name":"Siyuan Wei","orcid":"https://orcid.org/0000-0001-5701-1729"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Siyuan Wei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049494501","display_name":"Youngwon Choi","orcid":"https://orcid.org/0000-0003-1120-6218"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Youngwon Choi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006866108","display_name":"Muhammad Wahi\u2010Anwar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Muhammad Wahi-Anwar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016756556","display_name":"Liza Shrestha","orcid":"https://orcid.org/0000-0001-5722-4409"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liza Shrestha","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103537675","display_name":"K.P. Wong","orcid":"https://orcid.org/0000-0002-6095-4645"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Koon-Pong Wong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5070464496","display_name":"Matthew S. Brown","orcid":"https://orcid.org/0000-0001-6805-8151"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthew S. Brown","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5044881983"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4725,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.64675133,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"62","last_page":"62"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9919000267982483,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.9818000197410583,"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/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.9706000089645386,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6812624931335449},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5708479881286621},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5425035357475281},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.5193760991096497},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5191018581390381},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4641232490539551},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44041427969932556},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.3343309164047241},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2647027373313904}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6812624931335449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5708479881286621},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5425035357475281},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.5193760991096497},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5191018581390381},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4641232490539551},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44041427969932556},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.3343309164047241},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2647027373313904},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2651992","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2651992","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Computer-Aided Diagnosis","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2101608218","https://openalex.org/W2623880299","https://openalex.org/W2734349601","https://openalex.org/W2761196163","https://openalex.org/W2952841966","https://openalex.org/W2979696139","https://openalex.org/W3044770281","https://openalex.org/W3177297046","https://openalex.org/W3183167267","https://openalex.org/W3189965344","https://openalex.org/W3194181604","https://openalex.org/W4200213948","https://openalex.org/W4281864575","https://openalex.org/W4296580612","https://openalex.org/W6620707391","https://openalex.org/W6800227909","https://openalex.org/W6805278595"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4324315429","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4366829857","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W2799953226","https://openalex.org/W2070902896","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Deep":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"are":[5],"widely":[6],"used":[7,160],"in":[8,20,62,79],"medical":[9],"image":[10],"segmentation,":[11],"but":[12],"they":[13],"tend":[14],"to":[15,47,56,91,96,106,161,231],"ignore":[16],"smaller":[17],"foreground":[18],"classes":[19],"the":[21,33,58,67,73,92,108,146,150,163,168,174,178,180,193,205,225,232],"training":[22,64],"process":[23],"and":[24,123,126,132,149,177],"thus":[25],"degrade":[26],"segmentation":[27,41,113,130,138,184,227],"accuracy,":[28],"which":[29,197],"is":[30,55],"known":[31],"as":[32],"class":[34,59,88,99,218],"imbalance":[35,60],"problem.":[36],"Central":[37],"venous":[38],"catheter":[39],"(CVC)":[40],"suffers":[42],"from":[43,204],"such":[44],"problems,":[45],"leading":[46],"low":[48],"accuracy.":[49],"The":[50,137,216],"purpose":[51],"of":[52,77,157,190,214],"this":[53],"study":[54],"address":[57],"problem":[61],"CNN":[63,164,182,208],"for":[65],"segmenting":[66],"right":[68],"internal":[69],"jugular":[70],"lines":[71],"(RIJLs),":[72],"most":[74],"common":[75],"type":[76],"CVCs,":[78],"chest":[80],"X-ray":[81],"(CXR)":[82],"images.":[83],"We":[84],"applied":[85],"an":[86],"inverse":[87,217],"frequency":[89,100,219],"weight":[90],"standard":[93],"Dice":[94,102,147,169,175,206,221],"loss":[95,103,148,176,222],"formulate":[97],"a":[98,187,199,211,229],"weighted":[101,220],"(CFDL)":[104],"function":[105,223],"train":[107],"CNNs.":[109],"A":[110,153],"U-net":[111,230],"based":[112],"model":[114,139],"was":[115,140],"constructed":[116],"with":[117,145,167,186,210,228],"multichannel":[118],"pre-processing,":[119],"including":[120,128],"normalization,":[121],"denoising,":[122],"histogram":[124],"equalization,":[125],"post-processing":[127],"thresholding":[129],"candidates":[131],"interpolating":[133],"discontinued":[134],"line":[135],"segmentations.":[136],"trained":[141],"on":[142,192],"CXR":[143],"images":[144,158],"CFDL":[151],"respectively.":[152],"separate":[154,194],"test":[155,195],"set":[156],"were":[159],"evaluate":[162],"output":[165],"performances":[166],"Similarity":[170],"Coefficient":[171],"(DSC).":[172],"Between":[173],"CFDL,":[179],"CFDL-trained":[181],"generated":[183],"results":[185],"mean":[188,212],"DSC":[189,213],"0.581":[191],"set,":[196],"indicates":[198],"statistically":[200],"significant":[201],"difference":[202],"(p=0.001)":[203],"loss-trained":[207],"outputs":[209],"0.562.":[215],"improved":[224],"RIJL":[226],"state-of-the-art":[233],"performance.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}