Video Anomaly Detection in Post-Procedural Use of Laparoscopic Videos

Endoscopic surgery leads to large amounts of recordings that have to either be stored completely or postprocessed to extract relevant frames. These recordings regularly contain long out-of-body scenes. This paper proposes to apply anomaly detection methods to detect these irrelevant scenes. A conditional generative adversarial networks (GAN) architecture is used to predict future video frames and classify these predictions with an anomaly score. To avoid the successful prediction of anomalous frames due to the good generalization capability of convolutional neural networks (CNNs) we enhance the optimization process with a negative training phase. The experimental results demonstrate promising results for out-of-body sequence detection with the proposed approach. The enhanced GAN training framework can improve the results of the prediction framework by a large margin. The negative training phase reduces the number of false negative (FN) predictions and is shown to counteract a common problem in anomaly detection methods based on convolutional neural networks (CNNs). The good performance in standard metrics also shows the suitability for clinical use.