Automated Segmentation of Microvessels in Intravascular OCT Images Using Deep Learning.
classification
deep learning
microvessel
optical coherence tomography
segmentation
Journal
Bioengineering (Basel, Switzerland)
ISSN: 2306-5354
Titre abrégé: Bioengineering (Basel)
Pays: Switzerland
ID NLM: 101676056
Informations de publication
Date de publication:
03 Nov 2022
03 Nov 2022
Historique:
received:
30
09
2022
revised:
27
10
2022
accepted:
02
11
2022
entrez:
10
11
2022
pubmed:
11
11
2022
medline:
11
11
2022
Statut:
epublish
Résumé
Microvessels in vascular plaque are associated with plaque progression and are found in plaque rupture and intra-plaque hemorrhage. To analyze this characteristic of vulnerability, we developed an automated deep learning method for detecting microvessels in intravascular optical coherence tomography (IVOCT) images. A total of 8403 IVOCT image frames from 85 lesions and 37 normal segments were analyzed. Manual annotation was performed using a dedicated software (OCTOPUS) previously developed by our group. Data augmentation in the polar (
Identifiants
pubmed: 36354559
pii: bioengineering9110648
doi: 10.3390/bioengineering9110648
pmc: PMC9687448
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : NIH HHS
ID : NIH R21 HL108263
Pays : United States
Organisme : NIH HHS
ID : NIH R01 HL114406
Pays : United States
Organisme : NIH HHS
ID : NIH R01 HL143484
Pays : United States
Organisme : NIH HHS
ID : NIH R01 RES219220
Pays : United States
Organisme : American Heart Association
ID : #20POST35210974/Juhwan Lee/2020
Organisme : NIH HHS
ID : NIH C06 RR12463
Pays : United States
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