Automated OCT A-line abdominal tissue classification using a hybrid MLP-CNN classifier during ventral hernia repair

Yaning Wang, Shuwen Wei, Justin D. Opfermann, Michael Kam, Hamed Saeidi, Michael H. Hsieh, Axel Krieger, Jin U. Kang

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

Abstract

We developed a fully automated abdominal tissue classification algorithm for swept-source OCT imaging using a hybrid multilayer perceptron (MLP) and convolutional neural network (CNN) classifier. For MLP, we incorporated an extensive set of features and a subset was chosen to improve network efficiency. For CNN, we designed a threechannel model combining the intensity information with depth-dependent optical properties of tissues. A rule-based decision fusion approach was applied to find more convincing predictions between these two portions. Our model was trained using ex vivo porcine samples, (∼200 B-mode images, ∼200,000 A-line signals), evaluated by a hold-out dataset. Compared to other algorithms, our classifiers achieve the highest accuracy of 0.9114 and precision of 0.9106. The promising results showed its feasibility for real-Time abdominal tissue sensing during robotic-Assisted laparoscopic OCT surgery.

Original languageEnglish (US)
Title of host publicationOptical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII
EditorsIsrael Gannot, Israel Gannot, Katy Roodenko
PublisherSPIE
ISBN (Electronic)9781510647770
DOIs
StatePublished - 2022
EventOptical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII 2022 - Virtual, Online
Duration: Feb 20 2022Feb 24 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11953
ISSN (Print)1605-7422

Conference

ConferenceOptical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII 2022
CityVirtual, Online
Period2/20/222/24/22

Keywords

  • abdominal tissue classification
  • deep learning
  • machine learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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