Automated Quantification of Subretinal Fluid in Central Serous Chorioretinopathy in 3D Optical Coherence Tomography Images
F.G. Venhuizen, M.B. Breukink, B. van Ginneken, M.J.J.P. van Grinsven, B. Bloemen, C.B. Hoyng, T. Theelen, C.J.F. Boon and C.I. Sánchez
in: Association for Research in Vision and Ophthalmology, 2015
Purpose: Central serous chorioretinopathy (CSC) is an ocular disorder characterized by serous retinal detachment and associated with fluid accumulation beneath the retina. Obtaining accurate measures on the size and volume of the fluid deposit may be an important biomarker to assess disease progression and treatment outcome. We developed a system for automatic volumetric quantification of subretinal fluid in optical coherence tomography (OCT). Methods: OCT images obtained from 15 patients with varying presence of subretinal fluid were selected from the clinic. A 3D region growing based algorithm was developed to segment the fluid after selecting an arbitrary seed point located in the fluid deposit. The obtained total volume, and the extent of the segmented fluid volume were compared to manual delineations made by two experienced human graders. Results: A high intra-class correlation coefficient (ICC) value (0.997) was obtained when comparing the fluid volume calculated by the proposed method with the volume delineated by the two graders. Similarly, the spatial overlap agreement on the obtained volumes, measured with the Dice similarity coefficient (DC), between the manual delineations and the software output was high (DC=0.87) and comparable to the overlap agreement between observersÃ¢â‚¬â„¢ delineations (DC=0.85). In addition, the quantification time was reduced substantially by a factor of 5 compared to manual assessment. The quantified values obtained by the algorithm were shown to be highly reproducible, obtaining a DC value of 0.99 and an ICC value of 0.98 when varying the seed point used for initializing the algorithm. Conclusions: An image analysis algorithm for the automatic quantification of subretinal fluid in OCT images of CSC patients was developed. The proposed algorithm is able to accurately quantify the extent of fluid deposits in a fast and reproducible manner, allowing accurate assessment of disease progression and treatment outcome.