Paper About Lens Irregularity Detection in Cataract Surgery Accepted at MICCAI 2021

Our paper about LensID – a CNN-RNN-based framework for irregularity detection in cataract surgery videos has been accepted at the prestigious MICCAI 2021 conference (International Conference on Medical Image Computing & Computer Assisted Intervention). Negin Ghamsarian will present the details of her work on this topic in the end of September.

Authors: Negin Ghamsarian, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, and Klaus Schoeffmann

Link: https://www.miccai2021.org/en/


Abstract: A critical complication after cataract surgery is the dislocation of the lens implant leading to vision deterioration and eye trauma. In order to reduce the risk of this complication, it is vital to discover the risk factors during the surgery. However, studying the relationship between lens dislocation and its suspicious risk factors using numerous videos is a time-extensive procedure. Hence, the surgeons demand an automatic approach to enable a larger-scale and, accordingly, more reliable study. In this paper, we propose a novel framework as the major step towards lens irregularity detection. In particular, we propose (I) an end-to-end recurrent neural network to recognize the lens-implantation phase and (II) a novel semantic segmentation network to segment the lens and pupil after the implantation phase. The phase recognition results reveal the effectiveness of the proposed surgical phase recognition approach. Moreover, the segmentation results confirm the proposed segmentation network’s effectiveness compared to the state-of-the-art rival approaches.