A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images

Philippi, Daniel (author), Rothaus, Kai (author), Castelli, Mauro (author)

Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different lesions inside the retina. AMD biomarkers enable experts to grade the AMD and could be used for therapy prognosis and individualized treatment decisions. In particular, intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED) are prominent biomarkers for grading neovascular AMD. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD early diagnosis by providing cross-sectional images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the excellent performance of convolutional neural network (CNN)-based methods, the task still presents some challenges due to relevant variations in the location, size, shape, and texture of the lesions. This work adopts a transformer-based method to automatically segment retinal lesion from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus Eye-Center, Muenster. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network’s architecture to increase its segmentation performance while maintaining its computational efficiency.

• Language: English
• Keywords: neuroscience, informatics, models
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: EF - School of Economics and Business
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 14 str.
• Numbering: Vol. 13, art. 517
• PID: 20.500.12556/RUL-147241
• UDC: 659.2:004
• ISSN on article: 2045-2322
• DOI: 10.1038/s41598-023-27616-1
• COBISS.SI-ID: 139976707
• Publication date in RUL: 27.06.2023

Record is a part of a journal

• Title: Scientific reports
• Shortened title: Sci. rep.
• Publisher: Nature Publishing Group
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: kognitivna znanost, informatika, modeli

Projects

• Funder: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Project number: UIDB/04152/2020

• Funder: ARRS - Slovenian Research Agency
• Project number: P5-0410
• Name: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe