Paper Info
Title
Frame-Based Classification of Operation Phases in Cataract Surgery Videos.
Abstract
Cataract surgeries are frequently performed to correct a lens opacification of the human eye, which usually appears in the course of aging. These surgeries are conducted with the help of a microscope and are typically recorded on video for later inspection and educational purposes. However, post-hoc visual analysis of video recordings is cumbersome and time-consuming for surgeons if there is no navigation support, such as bookmarks to specific operation phases. To prepare the way for an automatic detection of operation phases in cataract surgery videos, we investigate the effectiveness of a deep convolutional neural network (CNN) to automatically assign video frames to operation phases, which can be regarded as a single-label multi-class classification problem. In absence of public datasets of cataract surgery videos, we provide a dataset of 21 videos of standardized cataract surgeries and use it to train and evaluate our CNN classifier. Experimental results display a mean F1-score of about 68% for frame-based operation phase classification, which can be further improved to 75% when considering temporal information of video frames in the CNN architecture.
Year
DOI
Venue
2018
10.1007/978-3-319-73603-7_20
Lecture Notes in Computer Science
Keywords
Field
DocType
Medical multimedia,Deep learning,Video analysis,Surgical workflow analysis
Human eye,Computer vision,Cataract surgery,Pattern recognition,Computer science,Convolutional neural network,Frame based,Artificial intelligence,Deep learning,Classifier (linguistics)
Conference
Volume
ISSN
Citations 
10704
0302-9743
2
PageRank 
References 
Authors
0.41
9
7
Name
Order
Citations
PageRank
Manfred Jürgen Primus1246.93
Doris Putzgruber-Adamitsch221.09
Mario Taschwer3769.39
Bernd Münzer49814.94
Yosuf El-Shabrawi521.09
László Böszörményi648566.44
Klaus Schoeffmann750963.01