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 state-of-the-art rival approaches.
Negin Ghamsarian, Mario Taschwer, Doris Putzgruber-Adamitsch, Stephanie Sarny, Yosuf El-Shabrawi, Klaus Schoeffmann