A Learning Based Microultrasound System for the Detection of Inflammation of the Gastrointestinal Tract

Shufan Yang, Christina Lemke, Benjamin F. Cox, Ian P. Newton, Inke Näthke, Sandy Cochran

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
312 Downloads (Pure)


Inflammation of the gastrointestinal (GI) tract accompanies several diseases, including Crohn's disease. Currently, video capsule endoscopy and deep bowel enteroscopy are the main means for direct visualisation of the bowel surface. However, the use of optical imaging limits visualisation to the luminal surface only, which makes early-stage diagnosis difficult. In this study, we propose a learning enabled microultrasound ( mu US) system that aims to classify inflamed and non-inflamed bowel tissues. mu US images of the caecum, small bowel and colon were obtained from mice treated with agents to induce inflammation. Those images were then used to train three deep learning networks and to provide a ground truth of inflammation status. The classification accuracy was evaluated using 10-fold evaluation and additional B-scan images. Our deep learning approach allowed robust differentiation between healthy tissue and tissue with early signs of inflammation that is not detectable by current endoscopic methods or by human inspection of the mu US images. The methods may be a foundation for future early GI disease diagnosis and enhanced management with computer-aided imaging.

Original languageEnglish
Pages (from-to)38-47
Number of pages10
JournalIEEE Transactions on Medical Imaging
Issue number1
Early online date3 Sept 2020
Publication statusPublished - Jan 2021


  • Computer-aided detection and diagnosis
  • gastrointestinal tract
  • neural network
  • ultrasound

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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