Dara D. Koozekanani

TL;DR: Qualitatively, the boundaries detected by the automated system generally agreed extremely well with the true retinal structure for the vast majority of OCT images, and a robust, quantitatively accurate system can be expected to improve patient care.

TL;DR: A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step and the bright lesions areclassified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms.

TL;DR: The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods.

TL;DR: A novel stopping criterion is presented that terminates the iterative process leading to higher vessel segmentation accuracy and is robust to the rate of new vessel pixel addition.

TL;DR: A challenge RETOUCH, which featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability, however, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentsation performance.