Automatic quantification of geographic atrophy in fundus autofluorescence images of Stargardt patients
C. Sánchez, S. Lambertus, B. Bloemen, N. Bax, F. Venhuizen, M. van Grinsven, B. van Ginneken, T. Theelen and C. Hoyng
Association for Research in Vision and Ophthalmology 2015.
Purpose: To evaluate an observer-independent image analysis algorithm that automatically quantifies the area of geographic atrophy in fundus autofluorescence images of Stargardt patients. Methods: Fundus autofluorescence images of 20 eyes of 20 Stargardt patients with presence of one delineated or patchy atrophy region in the macular area were selected. An image analysis algorithm was developed to automatically segment the area of atrophy starting from an arbitrarily selected seed point inside the atrophy region. The method was based on a combination of region growing algorithm and a dynamic, user-independent threshold selection procedure using Otsu thresholding. In order to assess the performance obtained by the proposed algorithm, manual annotations were made by an experienced human grader. The grader manually delineated the atrophy areas on the same set of images using dedicated software developed for this task. Results: A high correlation was observed between the manual area measurements and the automatically quantified values obtained by the proposed algorithm, with a mean intra-class correlation coefficient (ICC) value larger than 0.89. In addition, the quantification time was reduced substantially by a factor of 27 compared to manual assessment. The output of the software was also shown to be independent of the user input and highly reproducible, with an ICC value larger than 0.99 between two executions of the algorithm at different time points and with different seed points. Conclusions: An image analysis algorithm for automatic quantification of geographic atrophy in autofluorescence images of Stargardt patients was developed. The proposed algorithm allows for precise, reproducible and fast quantification of the atrophy area, providing an accurate procedure to measure disease progression and assess potential therapies in large dataset analyses independent of human observers.