NDLI: Learning the representation of instrument images in laparoscopy videos

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Learning the representation of instrument images in laparoscopy videos

Content Provider	IET Digital Library
Author	Kletz, Sabrina Schoeffmann, Klaus Husslein, Heinrich
Abstract	Automatic recognition of instruments in laparoscopy videos poses many challenges that need to be addressed, like identifying multiple instruments appearing in various representations and in different lighting conditions, which in turn may be occluded by other instruments, tissue, blood, or smoke. Considering these challenges, it may be beneficial for recognition approaches that instrument frames are first detected in a sequence of video frames for further investigating only these frames. This pre-recognition step is also relevant for many other classification tasks in laparoscopy videos, such as action recognition or adverse event analysis. In this work, the authors address the task of binary classification to recognise video frames as either instrument or non-instrument images. They examine convolutional neural network models to learn the representation of instrument frames in videos and take a closer look at learned activation patterns. For this task, GoogLeNet together with batch normalisation is trained and validated using a publicly available dataset for instrument count classifications. They compared transfer learning with learning from scratch and evaluate on datasets from cholecystectomy and gynaecology. The evaluation shows that fine-tuning a pre-trained model on the instrument and non-instrument images is much faster and more stable in learning than training a model from scratch.
Starting Page	197
Ending Page	203
Page Count	7
Volume Number	6
e-ISSN	20533713
Issue Number	Issue 6, Dec (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/htl/6/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/htl.2019.0077
Journal	Healthcare Technology Letters
Publisher	The Institution of Engineering and Technology
Publisher Date	2019-11-26
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Action Recognition Adverse Event Analysis Automatic Recognition Binary Classification Biology And Medical Computing Biomedical Measurement And Imaging Cholecystectomy Classification Tasks Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network GoogLeNet Gynaecology Health Physics Image Classification Image Motion Analysis Image Recognition Image Representation Image Sequence Instrument Count Classifications Instrument Frames Instrument Image Representation Laparoscopy Videos Learned Activation Pattern Learning in AI Medical And Biomedical Uses of Field Medical Image Processing Neural Computing Technique Noninstrument Image Object Detection Patient Diagnostic Method And Instrumentation Radiations Radioactivity Recognition Approach Surfactant Transfer Learning Video Frames Video Signal Processing
Content Type	Text
Resource Type	Article

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