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Automatika : journal for control, measurement, electronics, computing and communications, Vol. 62 No. 3-4, 2021.

Original scientific paper

https://doi.org/10.1080/00051144.2021.1973298

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

Martina Melinščak orcid id orcid.org/0000-0001-5128-3213 ; Department of Mechanical Engineering, Karlovac University of Applied Sciences, Karlovac, Croatia
Marin Radmilović orcid id orcid.org/0000-0003-2847-0525 ; Department of Ophthalmology, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
Zoran Vatavuk ; Department of Ophthalmology, Sestre milosrdnice University Hospital Center, Zagreb, Croatia
Sven Lončarić ; Department of Electronic Systems and Information Processing, Faculty of Electrical Engineering and Computing, Zagreb, Croatia

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Abstract

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.

Keywords

Annotated retinal OCT images; images database; automatic image segmentation; deep learning; age-related macular degeneration

Hrčak ID:

269851

URI

https://hrcak.srce.hr/269851

Publication date:

20.10.2021.

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