Can I read Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features on EtoBox?

Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features by Loukas, Constantinos is a scholarly article available to read on EtoBox.

What is Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features about?

Recognizing the phases of a laparoscopic surgery (LS) operation form its video constitutes a fundamental step for efficient content representation, indexing and retrieval in surgical video databases. In the literature, most techniques focus on phase segmentation of the entire LS video using hand-crafted visual features, instrument usage signals, and recently convolutional neural networks (CNNs). In this paper we address the problem of phase recognition of short video shots (10s) of the operation, without utilizing information about the preceding/forthcoming video frames, their phase labels or the instruments used. We investigate four state-of-the-art CNN architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature extraction via transfer learning. Visual saliency was employed for selecting the most informative region of the image as input to the CNN. Video shot representation was based on two temporal pooling mechanisms. Most importantly, we investigate the role of 'elapsed time' (from the beginning of the operation), and we show that inclusion of this feature can increase performance dramatically (69% vs. 75% mean accuracy). Finally, a long short-term memory (LSTM) networ

Author

Loukas, Constantinos

Published

2018

Language

EN