Our abstract/talk on iris and pupil segmentation in cataract surgery videos has been accepted for presentation at the ISBI 2020 conference.
Title: Pixel-Based Iris and Pupil Segmentation in Cataract Surgery Videos Using Mask R-CNN
Authors: Natalia Sokolova, Mario Taschwer, Klaus Schoeffmann
Abstract: Cataract surgery replaces the eye lens with an artificial one and is one of the most common surgical procedures performed worldwide. These surgeries can be recorded using a microscope camera and resulting videos stored for educational or documentary purposes as well as for automated post-operative analysis for detecting adverse events or complications. As pupil reactions (dilation or constriction) may lead to complications during surgery, automatic localization of pupil and iris in cataract surgery videos is a necessary preprocessing step for automated analysis. The problems of recognition, localization and tracking of eyes in medical images or videos have already been studied in the literature. However, none of these approaches used pixel-based segmentation, which would allow to localize pupil and iris in a sufficiently accurate way for further automated analysis. In this work, we investigate pixel-based pupil and iris segmentation by a region-based convolutional neural network (Mask R-CNN), which has not been applied to this problem before, to the best of our knowledge. We evaluate the performance of Mask R-CNN with different backbone networks for a manually annotated image dataset. Our method achieves at least 80% of Intersection over Union (IoU) for each iris example and at least 85% IoU for each pupil example in the test dataset.