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Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Khateri, P; Koottungal, T; Wong, D; Strauss, RW; Janeschitz-Kriegl, L; Pfau, M; Schmetterer, L; Scholl, HPN.
Looking outside the box with a pathology aware AI approach for analyzing OCT retinal images in Stargardt disease.
Sci Rep. 2025; 15(1):4739
Doi: 10.1038/s41598-025-85213-w
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- Co-authors Med Uni Graz
- Strauß Rupert
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- Abstract:
- Stargardt disease type 1 (STGD1) is a genetic disorder that leads to progressive vision loss, with no approved treatments currently available. The development of effective therapies faces the challenge of identifying appropriate outcome measures that accurately reflect treatment benefits. Optical Coherence Tomography (OCT) provides high-resolution retinal images, serving as a valuable tool for deriving potential outcome measures, such as retinal thickness. However, automated segmentation of OCT images, particularly in regions disrupted by degeneration, remains complex. In this study, we propose a deep learning-based approach that incorporates a pathology-aware loss function to segment retinal sublayers in OCT images from patients with STGD1. This method targets relatively unaffected regions for sublayer segmentation, ensuring accurate boundary delineation in areas with minimal disruption. In severely affected regions, identified by a box detection model, the total retina is segmented as a single layer to avoid errors. Our model significantly outperforms standard models, achieving an average Dice coefficient of [Formula: see text] for total retina and [Formula: see text] for retinal sublayers. The most substantial improvement was in the segmentation of the photoreceptor inner segment, with Dice coefficient increasing by [Formula: see text]. This approach provides a balance between granularity and reliability, making it suitable for clinical application in tracking disease progression and evaluating therapeutic efficacy.
- Find related publications in this database (using NLM MeSH Indexing)
- Tomography, Optical Coherence - methods
- Stargardt Disease - diagnostic imaging
- Humans - administration & dosage
- Retina - diagnostic imaging, pathology
- Deep Learning - administration & dosage
- Image Processing, Computer-Assisted - methods
- Macular Degeneration - diagnostic imaging, congenital, pathology
- Find related publications in this database (Keywords)
- Stargardt Disease
- Optical Coherence Tomography
- Deep Learning
- Retina Segmentation
- Pathology-Aware Loss Function
- Automated Image Analysis