ArticleTranslational vision science & technology2020
Merging Information From Infrared and Autofluorescence Fundus Images for Monitoring of Chorioretinal Atrophic Lesions.
Article in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography.Scientific reports · 2024Article
- Recent Advances in Imaging Macular Atrophy for Late-Stage Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2023Review
- Recent Advances and Clinical Application of Color Scanning Laser Ophthalmoscope.Journal of clinical medicine · 2021Review
- Morphological Changes and Prognostic Factors before and after Photodynamic Therapy for Central Serous Chorioretinopathy.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Deep Learning-Based Classification of Inherited Retinal Diseases Using Fundus Autofluorescence.Journal of clinical medicine · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
Purpose: To develop a method for automated detection and progression analysis of chorioretinal atrophic lesions using the combined information of standard infrared (IR) and autofluorescence (AF) fundus images. Methods: Eighteen eyes (from 16 subjects) with punctate inner choroidopathy were analyzed. Macular IR and blue AF images were acquired in all eyes with a Spectralis HRA+OCT device (Heidelberg Engineering, Heidelberg, Germany). Two clinical experts manually segmented chorioretinal lesions on the AF image. AF images were aligned to the corresponding IR. Two random forest models were trained to classify pixels of lesions, one based on the AF image only, the other based on the aligned IR-AF. The models were validated using a leave-one-out cross-validation and were tested against the manual segmentation to compare their performance. A time series from one eye was identified and used to evaluate the method based on the IR-AF in a case study. Results: The method based on the AF images correctly classified 95% of the pixels (i.e., in vs. out of the lesion) with a Dice's coefficient of 0.80. The method based on the combined IR-AF correctly classified 96% of the pixels with a Dice's coefficient of 0.84. Conclusions: The automated segmentation of chorioretinal lesions using IR and AF shows closer alignment to manual segmentation than the same method based on AF only. Merging information from multimodal images improves the automatic and objective segmentation of chorioretinal lesions even when based on a small dataset. Translational Relevance: Merged information from multimodal images improves segmentation performance of chorioretinal lesions.
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