_version_ 1852019488587776000
author Seok Jin Bang (13028202)
author2 Yong-Tae Kim (249037)
Young Jae Kim (8098697)
Kwang Gi Kim (10654900)
author2_role author
author
author
author_facet Seok Jin Bang (13028202)
Yong-Tae Kim (249037)
Young Jae Kim (8098697)
Kwang Gi Kim (10654900)
author_role author
dc.creator.none.fl_str_mv Seok Jin Bang (13028202)
Yong-Tae Kim (249037)
Young Jae Kim (8098697)
Kwang Gi Kim (10654900)
dc.date.none.fl_str_mv 2025-06-09T17:37:36Z
dc.identifier.none.fl_str_mv 10.1371/journal.pone.0324021.g005
dc.relation.none.fl_str_mv https://figshare.com/articles/figure/Lesions_segmentation_results_in_each_model_/29271981
dc.rights.none.fl_str_mv CC BY 4.0
info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv Medicine
Space Science
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
second leading cause
fully connected network
dice similarity coefficient
validated using five
fcn model exhibited
xlink "> stroke
ischemic stroke collected
constructed using data
net model demonstrated
artificial intelligence models
apparent diffusion coefficient
lesion segmentation performance
dsc ), accuracy
adc images based
lesion segmentation
fcn ),
conducted using
image data
evaluated based
adc based
stroke diagnosis
weighted imaging
utilizing images
training models
trained models
retrospective design
future research
fold cross
different b
deaths worldwide
comparing diffusion
comparative analysis
adc images
360 patients
dc.title.none.fl_str_mv Lesions segmentation results in each model.
dc.type.none.fl_str_mv Image
Figure
info:eu-repo/semantics/publishedVersion
image
description <p>Comparisone of apparent diffusion coefficient and diffusion-weighted image predictions from different images.</p>
eu_rights_str_mv openAccess
id Manara_dacb71ff308f4be28c5fc405caef2bef
identifier_str_mv 10.1371/journal.pone.0324021.g005
network_acronym_str Manara
network_name_str ManaraRepo
oai_identifier_str oai:figshare.com:article/29271981
publishDate 2025
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv CC BY 4.0
spelling Lesions segmentation results in each model.Seok Jin Bang (13028202)Yong-Tae Kim (249037)Young Jae Kim (8098697)Kwang Gi Kim (10654900)MedicineSpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsecond leading causefully connected networkdice similarity coefficientvalidated using fivefcn model exhibitedxlink "> strokeischemic stroke collectedconstructed using datanet model demonstratedartificial intelligence modelsapparent diffusion coefficientlesion segmentation performancedsc ), accuracyadc images basedlesion segmentationfcn ),conducted usingimage dataevaluated basedadc basedstroke diagnosisweighted imagingutilizing imagestraining modelstrained modelsretrospective designfuture researchfold crossdifferent bdeaths worldwidecomparing diffusioncomparative analysisadc images360 patients<p>Comparisone of apparent diffusion coefficient and diffusion-weighted image predictions from different images.</p>2025-06-09T17:37:36ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0324021.g005https://figshare.com/articles/figure/Lesions_segmentation_results_in_each_model_/29271981CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292719812025-06-09T17:37:36Z
spellingShingle Lesions segmentation results in each model.
Seok Jin Bang (13028202)
Medicine
Space Science
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
second leading cause
fully connected network
dice similarity coefficient
validated using five
fcn model exhibited
xlink "> stroke
ischemic stroke collected
constructed using data
net model demonstrated
artificial intelligence models
apparent diffusion coefficient
lesion segmentation performance
dsc ), accuracy
adc images based
lesion segmentation
fcn ),
conducted using
image data
evaluated based
adc based
stroke diagnosis
weighted imaging
utilizing images
training models
trained models
retrospective design
future research
fold cross
different b
deaths worldwide
comparing diffusion
comparative analysis
adc images
360 patients
status_str publishedVersion
title Lesions segmentation results in each model.
title_full Lesions segmentation results in each model.
title_fullStr Lesions segmentation results in each model.
title_full_unstemmed Lesions segmentation results in each model.
title_short Lesions segmentation results in each model.
title_sort Lesions segmentation results in each model.
topic Medicine
Space Science
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
second leading cause
fully connected network
dice similarity coefficient
validated using five
fcn model exhibited
xlink "> stroke
ischemic stroke collected
constructed using data
net model demonstrated
artificial intelligence models
apparent diffusion coefficient
lesion segmentation performance
dsc ), accuracy
adc images based
lesion segmentation
fcn ),
conducted using
image data
evaluated based
adc based
stroke diagnosis
weighted imaging
utilizing images
training models
trained models
retrospective design
future research
fold cross
different b
deaths worldwide
comparing diffusion
comparative analysis
adc images
360 patients