Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound

Yang, Shufan; Lemke, Christina; Cox, Ben F.; Newton, Ian P.; Cochran, Sandy; Nathke, Inke

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.

Citation

Yang, S., Lemke, C., Cox, B. F., Newton, I. P., Cochran, S., & Nathke, I. (2020, September). Deep Compressed Sensing for Characterizing Inflammation Severity with Microultrasound. Presented at 2020 IEEE International Ultrasonics Symposium (IUS), Las Vegas, NV, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2020 IEEE International Ultrasonics Symposium (IUS)
• Start Date: Sep 7, 2020
• End Date: Sep 11, 2020
• Acceptance Date: Aug 18, 2020
• Online Publication Date: Nov 17, 2020
• Publication Date: Sep 7, 2020
• Deposit Date: Mar 11, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Book Title: 2020 IEEE International Ultrasonics Symposium (IUS)
• ISBN: 9781728154480
• DOI: https://doi.org/10.1109/ius46767.2020.9251280
• Keywords: B-scan images, Deep Learning, Generative Adversarial Network (GAN), Microultrasound
• Public URL: http://researchrepository.napier.ac.uk/Output/2752430