Jay Chhablani

Lingyun Wang, Bingjie Wang, Jay Chhablani et al.

Objective: To achieve accurate 3-D reconstruction and quantitative analysis of human retinal vasculature from a single optical coherence tomography angiography (OCTA) scan. Methods: We introduce Freqformer, a novel Transformer-based model featuring a dual-branch architecture that integrates a Transformer layer for capturing global spatial context with a complex-valued frequency-domain module designed for adaptive frequency enhancement. Freqformer was trained using single depth-plane OCTA images, utilizing volumetrically merged OCTA as the ground truth. Performance was evaluated quantitatively through 2-D and 3-D image quality metrics. 2-D networks and their 3-D counterparts were compared to assess the differences between enhancing volume slice by slice and enhancing it by 3-D patches. Furthermore, 3-D quantitative vascular metrics were conducted to quantify human retinal vasculature. Results: Freqformer substantially outperformed existing convolutional neural networks and Transformer-based methods, achieving superior image metrics. Importantly, the enhanced OCTA volumes show strong correlation with the merged volumes on vascular segment count, density, length, and flow index, further underscoring its reliability for quantitative vascular analysis. 3-D counterparts did not yield additional gains in image metrics or downstream 3-D vascular quantification but incurred nearly an order-of-magnitude longer inference time, supporting our 2-D slice-wise enhancement strategy. Additionally, Freqformer showed excellent generalization capability on larger field-of-view scans, surpassing the quality of conventional volumetric merging methods. Conclusion: Freqformer reliably generates high-definition 3-D retinal microvasculature from single-scan OCTA, enabling precise vascular quantification comparable to standard volumetric merging methods.

Shanmukh Reddy Manne, Kiran Kumar Vupparaboina, Gowtham Chowdary Gudapati et al.

A variety of vision ailments are associated with geographic atrophy (GA) in the foveal region of the eye. In current clinical practice, the ophthalmologist manually detects potential presence of such GA based on fundus autofluorescence (FAF) images, and hence diagnoses the disease, when relevant. However, in view of the general scarcity of ophthalmologists relative to the large number of subjects seeking eyecare, especially in remote regions, it becomes imperative to develop methods to direct expert time and effort to medically significant cases. Further, subjects from either disadvantaged background or remote localities, who face considerable economic/physical barrier in consulting trained ophthalmologists, tend to seek medical attention only after being reasonably certain that an adverse condition exists. To serve the interest of both the ophthalmologist and the potential patient, we plan a screening step, where healthy and diseased eyes are algorithmically differentiated with limited input from only optometrists who are relatively more abundant in number. Specifically, an early treatment diabetic retinopathy study (ETDRS) grid is placed by an optometrist on each FAF image, based on which sectoral statistics are automatically collected. Using such statistics as features, healthy and diseased eyes are proposed to be classified by training an algorithm using available medical records. In this connection, we demonstrate the efficacy of support vector machines (SVM). Specifically, we consider SVM with linear as well as radial basis function (RBF) kernel, and observe satisfactory performance of both variants. Among those, we recommend the latter in view of its slight superiority in terms of classification accuracy (90.55% at a standard training-to-test ratio of 80:20), and practical class-conditional costs.