Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound

Yang, S. , Lemke, C. , Cox, B. F., Newton, I. P., Cochran, S. and Nathke, I. (2020) Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound. In: 2020 IEEE International Ultrasonics Symposium (IUS), 07-11 Sep 2020, ISBN 9781728154480 (doi: 10.1109/IUS46767.2020.9251280)

Abstract

With histological information on inflammation status as the ground truth, deep learning methods can be used as a classifier to distinguish different stages of bowel inflammation based on microultrasound (μUS) B-scan images. However, it is extremely time consuming and animal usage is high to obtain a balanced data set for every stage of inflammation. In this study, we describe a deep compressed sensing method to increase the number of B-scan images for inflammation studies without use of additional animals. In this way, training data can be quickly augmented. The fidelity of the synthesized data is evaluated using both qualitative and quantitative methods. We find that the synthetic data have high structural similarity when compared with original B-scan images. Further evaluation, such as finding the correlation of μUS and microscopy images and calculating attenuation coefficient, will be investigated in future to provide better understanding.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cochran, Professor Sandy and Lemke, Dr Christina and Yang, Dr Shufan and Cox, Dr Benjamin F
• Authors: Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Cochran, S., and Nathke, I.
• College/School: College of Science and Engineering > School of Engineering; College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 1948-5727
• ISBN: 9781728154480
• Copyright Holders: Copyright © 2020 IEEE
• First Published: First published in 2020 IEEE International Ultrasonics Symposium (IUS)
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
173138		Sonopill: minimally invasive gastrointestinal diagnosis and therapy	Alexander Cochran	Engineering and Physical Sciences Research Council (EPSRC)	EP/K034537/2	ENG - Systems Power & Energy