Deep compressed sensing for characterizing inflammation severity with microultrasound

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIUS 2020 - International Ultrasonics Symposium, Proceedings
PublisherIEEE
Number of pages4
ISBN (Electronic)9781728154480
ISBN (Print)9781728154497
DOIs
Publication statusPublished - 17 Nov 2020
Event2020 IEEE International Ultrasonics Symposium, IUS 2020 - Las Vegas, United States
Duration: 7 Sep 202011 Sep 2020

Publication series

NameIEEE International Ultrasonics Symposium, IUS
Volume2020-September
ISSN (Print)1948-5719
ISSN (Electronic)1948-5727

Conference

Conference2020 IEEE International Ultrasonics Symposium, IUS 2020
CountryUnited States
CityLas Vegas
Period7/09/2011/09/20

Keywords

  • B-scan images
  • Deep Learning
  • Generative Adversarial Network (GAN)
  • Microultrasound

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