{"id":"https://openalex.org/W7166231999","doi":"https://doi.org/10.48550/arxiv.2606.26898","title":"Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology","display_name":"Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166231999","doi":"https://doi.org/10.48550/arxiv.2606.26898"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26898","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26898","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.26898","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5025231981","display_name":"Kyriaki-Margarita Bintsi","orcid":"https://orcid.org/0000-0003-3875-7757"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bintsi, Kyriaki-Margarita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139412350","display_name":"Sparsh Makharia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Makharia, Sparsh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089640308","display_name":"Ya\u00ebl Balbastre","orcid":"https://orcid.org/0000-0001-8758-9978"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balbastre, Ya\u00ebl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139435252","display_name":"Joselyn Romero Avila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Avila, Joselyn Romero","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029295973","display_name":"Julia F. Lehman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lehman, Julia F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011073185","display_name":"Suzanne N. Haber","orcid":"https://orcid.org/0000-0002-5237-1941"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haber, Suzanne N.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082002582","display_name":"Anastasia Yendiki","orcid":"https://orcid.org/0000-0003-1386-3828"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yendiki, Anastasia","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":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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9958999752998352,"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/T11304","display_name":"Advanced Neuroimaging Techniques and Applications","score":0.9958999752998352,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.000699999975040555,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10129","display_name":"Glioma Diagnosis and Treatment","score":0.00039999998989515007,"subfield":{"id":"https://openalex.org/subfields/2716","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.714900016784668},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.7135999798774719},{"id":"https://openalex.org/keywords/tractography","display_name":"Tractography","score":0.6607999801635742},{"id":"https://openalex.org/keywords/bundle","display_name":"Bundle","score":0.6430000066757202},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5907999873161316},{"id":"https://openalex.org/keywords/tracer","display_name":"TRACER","score":0.5594000220298767},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4494999945163727},{"id":"https://openalex.org/keywords/fiber","display_name":"Fiber","score":0.40959998965263367},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.40389999747276306}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7522000074386597},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.714900016784668},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.7135999798774719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6876999735832214},{"id":"https://openalex.org/C84787856","wikidata":"https://www.wikidata.org/wiki/Q3076659","display_name":"Tractography","level":4,"score":0.6607999801635742},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.6430000066757202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5907999873161316},{"id":"https://openalex.org/C2778863792","wikidata":"https://www.wikidata.org/wiki/Q16909918","display_name":"TRACER","level":2,"score":0.5594000220298767},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5224999785423279},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4494999945163727},{"id":"https://openalex.org/C519885992","wikidata":"https://www.wikidata.org/wiki/Q161","display_name":"Fiber","level":2,"score":0.40959998965263367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C149550507","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion MRI","level":3,"score":0.3749000132083893},{"id":"https://openalex.org/C146710177","wikidata":"https://www.wikidata.org/wiki/Q1154996","display_name":"Fiber bundle","level":3,"score":0.3741999864578247},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.34470000863075256},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.321399986743927},{"id":"https://openalex.org/C2992283565","wikidata":"https://www.wikidata.org/wiki/Q899360","display_name":"Diffusion imaging","level":4,"score":0.30709999799728394},{"id":"https://openalex.org/C3019060180","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automated method","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C82233179","wikidata":"https://www.wikidata.org/wiki/Q2054500","display_name":"Partial volume","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C64208038","wikidata":"https://www.wikidata.org/wiki/Q5856","display_name":"Synthetic fiber","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.250900000333786},{"id":"https://openalex.org/C97820695","wikidata":"https://www.wikidata.org/wiki/Q387749","display_name":"Human Connectome Project","level":3,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26898","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26898","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.26898","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26898","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"MRI":[1],"(dMRI)":[2],"tractography":[3,70],"enables":[4],"non-invasive":[5],"reconstruction":[6],"of":[7,20,43],"white-matter":[8],"pathways,":[9],"but":[10],"its":[11],"accuracy":[12],"is":[13,106],"fundamentally":[14],"limited":[15],"by":[16],"indirect,":[17],"low-resolution":[18],"measurements":[19],"axonal":[21],"organization.":[22],"Tracer":[23],"injection":[24],"studies":[25],"in":[26,46,60],"non-human":[27],"primates":[28],"provide":[29],"a":[30,51,72],"gold":[31],"standard":[32],"for":[33,55,80,146],"validating":[34],"dMRI":[35,69],"tractography.":[36],"This,":[37],"however,":[38],"requires":[39],"time-consuming":[40],"manual":[41],"annotation":[42],"fiber":[44,57,124],"bundles":[45],"histology":[47],"sections.":[48],"We":[49],"propose":[50],"synthetic-data":[52],"augmented":[53],"framework":[54],"automated":[56],"bundle":[58,125],"segmentation":[59],"macaque":[61],"tracer":[62],"histology.":[63],"Our":[64],"approach":[65,151],"uses":[66],"ex":[67],"vivo":[68],"as":[71],"generative":[73],"prior":[74],"to":[75,128,140,155],"synthesize":[76],"2D":[77,104],"image":[78],"patches":[79],"training.":[81],"This":[82],"provides":[83],"us":[84],"with":[85,93,130,135],"sufficiently":[86],"realistic":[87],"foreground":[88],"texture,":[89],"which":[90],"we":[91],"compose":[92],"backgrounds":[94],"from":[95],"blockface":[96],"photos":[97],"and":[98,111,123],"diversify":[99],"via":[100],"domain":[101],"randomization.":[102],"A":[103],"U-Net":[105],"trained":[107],"on":[108,115],"mixed":[109],"real":[110,131,147],"synthetic":[112,136],"patches.":[113],"Experiments":[114],"held-out":[116],"brains":[117,122],"demonstrate":[118],"improved":[119],"generalization":[120],"across":[121],"densities":[126],"compared":[127],"training":[129],"data":[132,137],"only.":[133],"Training":[134],"only":[138],"leads":[139],"poor":[141],"performance,":[142],"underscoring":[143],"the":[144,156],"need":[145],"supervision.":[148],"Overall,":[149],"our":[150],"achieves":[152],"performance":[153],"comparable":[154],"state-of-the-art":[157],"while":[158],"requiring":[159],"3x":[160],"less":[161],"manually":[162],"annotated":[163],"data.":[164]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-27T00:00:00"}