Hard frame detection for the automated clipping of surgical nasal endoscopic video

The automated clipping of surgical nasal endoscopic video is a challenging task because there are many hard frames that have indiscriminative visual features which lead to misclassification. Prior works mainly aim to classify these hard frames along with other frames, and it would seriously affect the performance of classification.

We propose a hard frame detection method using a convolutional LSTM network (called HFD-ConvLSTM) to remove invalid video frames automatically. Firstly, a new separator based on the coarse-grained classifier is defined to remove the invalid frames. Meanwhile, the hard frames are detected via measuring the blurring score of a video frame. Then, the squeeze-and-excitation is used to select the informative spatial-temporal features of endoscopic videos and further classify the video frames with a fine-grained ConvLSTM learning from the reconstructed training set with hard frames.

We justify the proposed solution through extensive experiments using 12 surgical videos (duration:8501 s). The experiments are performed on both hard frame detection and video frame classification. Nearly 88.3% fuzzy frames can be detected and the classification accuracy is boosted to 95.2%. HFD-ConvLSTM achieves superior performance compared to other methods.

HFD-ConvLSTM provides a new paradigm for video clipping by breaking the complex clipping problem into smaller, more easily managed 2-classification problems. Our investigation reveals that the hard framed detection based on blurring score calculation is effective for nasal endoscopic video clipping.