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

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

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.

• Jezik: Angleški jezik
• Ključne besede: neuroscience, informatics, models
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 14 str.
• Številčenje: Vol. 13, art. 517
• PID: 20.500.12556/RUL-147241
• UDK: 659.2:004
• ISSN pri članku: 2045-2322
• DOI: 10.1038/s41598-023-27616-1
• COBISS.SI-ID: 139976707
• Datum objave v RUL: 27.06.2023

Gradivo je del revije

• Naslov: Scientific reports
• Skrajšan naslov: Sci. rep.
• Založnik: Nature Publishing Group
• ISSN: 2045-2322
• COBISS.SI-ID: 18727432

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: kognitivna znanost, informatika, modeli

Projekti

• Financer: FCT - Fundação para a Ciência e a Tecnologia, I.P.
• Številka projekta: UIDB/04152/2020

• Financer: ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
• Številka projekta: P5-0410
• Naslov: Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe