Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning

Hongbo Luo, Shuying Li, Yifeng Zeng, Hassam Cheema, Ebunoluwa Otegbeye, Safee Ahmed, William C. Chapman, Matthew Mutch, Chao Zhou, Quing Zhu

Research output: Contribution to journalArticlepeer-review

Abstract

Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent advances in convolutional neural networks (CNN) have enabled application in ophthalmology, cardiology, and gastroenterology malignancy detection with high sensitivity and specificity. Here, we describe a miniaturized OCT catheter and a residual neural network (ResNet)-based deep learning model manufactured and trained to perform automatic image processing and real-time diagnosis of the OCT images. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~7 μm, and an axial resolution of ~6 μm. A customized ResNet is utilized to classify OCT catheter colorectal images. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.

Original languageEnglish
Article numbere202100349
JournalJournal of Biophotonics
Volume15
Issue number6
DOIs
StatePublished - Jun 2022

Keywords

  • ResNet
  • catheter
  • colorectal cancer
  • deep learning
  • optical coherence tomography

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