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Pregled bibliografske jedinice broj: 1140929

Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation


Melinščak, Martina; Radmilović, Marin; Vatavuk, Zoran; Lončarić, Sven
Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation // Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 62 (2021), 3; 375-385 doi:10.1080/00051144.2021.1973298 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1140929 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation

Autori
Melinščak, Martina ; Radmilović, Marin ; Vatavuk, Zoran ; Lončarić, Sven

Izvornik
Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije (0005-1144) 62 (2021), 3; 375-385

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
annotated retinal OCT images ; images database ; automatic image segmentation ; deep learning ; age-related macular degeneration

Sažetak
Optical coherence tomography (OCT) images of the retina provide a structural representation and give an insight into the pathological changes present in age-related macular degeneration (AMD). Due to the three-dimensionality and complexity of the images, manual analysis of pathological features is difficult, time-consuming, and prone to subjectivity. Computer analysis of 3D OCT images is necessary to enable automated quantitative measuring of the features, objectively and repeatedly. As supervised and semi-supervised learning-based automatic segmentation depends on the training data and quality of annotations, we have created a new database of annotated retinal OCT images – the AROI database. It consists of 1136 images with annotations for pathological changes (fluid accumulation and related findings) and basic structures (layers) in patients with AMD. Inter- and intra-observer errors have been calculated in order to enable the validation of developed algorithms in relation to human variability. Also, we have performed the automatic segmentation with standard U-net architecture and two state-of-the- art architectures for medical image segmentation to set a baseline for further algorithm development and to get insight into challenges for automatic segmentation. To facilitate and encourage further research in the field, we have made the AROI database openly available.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti



POVEZANOST RADA


Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb,
Stomatološki fakultet, Zagreb,
KBC "Sestre Milosrdnice",
Veleučilište u Karlovcu

Profili:

Avatar Url Zoran Vatavuk (autor)

Avatar Url Sven Lončarić (autor)

Avatar Url Martina Melinščak (autor)

Poveznice na cjeloviti tekst rada:

doi www.tandfonline.com

Citiraj ovu publikaciju:

Melinščak, Martina; Radmilović, Marin; Vatavuk, Zoran; Lončarić, Sven
Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation // Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 62 (2021), 3; 375-385 doi:10.1080/00051144.2021.1973298 (međunarodna recenzija, članak, znanstveni)
Melinščak, M., Radmilović, M., Vatavuk, Z. & Lončarić, S. (2021) Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation. Automatika : časopis za automatiku, mjerenje, elektroniku, računarstvo i komunikacije, 62 (3), 375-385 doi:10.1080/00051144.2021.1973298.
@article{article, author = {Melin\v{s}\v{c}ak, Martina and Radmilovi\'{c}, Marin and Vatavuk, Zoran and Lon\v{c}ari\'{c}, Sven}, year = {2021}, pages = {375-385}, DOI = {10.1080/00051144.2021.1973298}, keywords = {annotated retinal OCT images, images database, automatic image segmentation, deep learning, age-related macular degeneration}, journal = {Automatika : \v{c}asopis za automatiku, mjerenje, elektroniku, ra\v{c}unarstvo i komunikacije}, doi = {10.1080/00051144.2021.1973298}, volume = {62}, number = {3}, issn = {0005-1144}, title = {Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation}, keyword = {annotated retinal OCT images, images database, automatic image segmentation, deep learning, age-related macular degeneration} }
@article{article, author = {Melin\v{s}\v{c}ak, Martina and Radmilovi\'{c}, Marin and Vatavuk, Zoran and Lon\v{c}ari\'{c}, Sven}, year = {2021}, pages = {375-385}, DOI = {10.1080/00051144.2021.1973298}, keywords = {annotated retinal OCT images, images database, automatic image segmentation, deep learning, age-related macular degeneration}, journal = {Automatika : \v{c}asopis za automatiku, mjerenje, elektroniku, ra\v{c}unarstvo i komunikacije}, doi = {10.1080/00051144.2021.1973298}, volume = {62}, number = {3}, issn = {0005-1144}, title = {Annotated retinal optical coherence tomography images (AROI) database for joint retinal layer and fluid segmentation}, keyword = {annotated retinal OCT images, images database, automatic image segmentation, deep learning, age-related macular degeneration} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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