  In cataract surgery, the operation is performed with the help of a
microscope. Since the microscope enables watching real-time surgery by up to
two people only, a major part of surgical training is conducted using the
recorded videos. To optimize the training procedure with the video content, the
surgeons require an automatic relevance detection approach. In addition to
relevance-based retrieval, these results can be further used for skill
assessment and irregularity detection in cataract surgery videos. In this
paper, a three-module framework is proposed to detect and classify the relevant
phase segments in cataract videos. Taking advantage of an idle frame
recognition network, the video is divided into idle and action segments. To
boost the performance in relevance detection, the cornea where the relevant
surgical actions are conducted is detected in all frames using Mask R-CNN. The
spatiotemporally localized segments containing higher-resolution information
about the pupil texture and actions, and complementary temporal information
from the same phase are fed into the relevance detection module. This module
consists of four parallel recurrent CNNs being responsible to detect four
relevant phases that have been defined with medical experts. The results will
then be integrated to classify the action phases as irrelevant or one of four
relevant phases. Experimental results reveal that the proposed approach
outperforms static CNNs and different configurations of feature-based and
end-to-end recurrent networks.
