{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:51:23Z","timestamp":1740149483345,"version":"3.37.3"},"reference-count":76,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004281","name":"National Science Center","doi-asserted-by":"publisher","award":["2019\/32\/T\/ST6\/00500"],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"This paper proposes an efficient segmentation of the preretinal area between the inner limiting membrane (ILM) and posterior cortical vitreous (PCV) of the human eye in an image obtained with the use of optical coherence tomography (OCT). The research was carried out using a database of three-dimensional OCT imaging scans obtained with the Optovue RTVue XR Avanti device. Various types of neural networks (UNet, Attention UNet, ReLayNet, LFUNet) were tested for semantic segmentation, their effectiveness was assessed using the Dice coefficient and compared to the graph theory techniques. Improvement in segmentation efficiency was achieved through the use of relative distance maps. We also show that selecting a larger kernel size for convolutional layers can improve segmentation quality depending on the neural network model. In the case of PVC, we obtain the effectiveness reaching up to 96.35%. The proposed solution can be widely used to diagnose vitreomacular traction changes, which is not yet available in scientific or commercial OCT imaging solutions.<\/jats:p>","DOI":"10.3390\/s21227521","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T01:51:53Z","timestamp":1636941113000},"page":"7521","source":"Crossref","is-referenced-by-count":9,"title":["Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2983-897X","authenticated-orcid":false,"given":"Agnieszka","family":"Stankiewicz","sequence":"first","affiliation":[{"name":"Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6035-7325","authenticated-orcid":false,"given":"Tomasz","family":"Marciniak","sequence":"additional","affiliation":[{"name":"Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9385-6080","authenticated-orcid":false,"given":"Adam","family":"Dabrowski","sequence":"additional","affiliation":[{"name":"Division of Electronic Systems and Signal Processing, Institute of Automatic Control and Robotics, Poznan University of Technology, 60-965 Poznan, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9540-9500","authenticated-orcid":false,"given":"Marcin","family":"Stopa","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7009-0406","authenticated-orcid":false,"given":"Elzbieta","family":"Marciniak","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Chair of Ophthalmology and Optometry, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 60-780 Poznan, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4084-7778","authenticated-orcid":false,"given":"Boguslaw","family":"Obara","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle upon Tyne NE4 5TG, UK"},{"name":"Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1016\/j.ophtha.2013.07.042","article-title":"The international vitreomacular traction study group classification of vitreomacular adhesion, traction, and macular hole","volume":"120","author":"Duker","year":"2013","journal-title":"Ophthalmology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1177\/112067210701700214","article-title":"Retinal imaging by spectral optical coherence tomography","volume":"17","author":"Kaluzny","year":"2007","journal-title":"Eur. 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