@inproceedings{673600c71dca40ec8fc12d1fc62821c5,
title = "Automated OCT A-line abdominal tissue classification using a hybrid MLP-CNN classifier during ventral hernia repair",
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.",
keywords = "abdominal tissue classification, deep learning, machine learning",
author = "Yaning Wang and Shuwen Wei and Opfermann, {Justin D.} and Michael Kam and Hamed Saeidi and Hsieh, {Michael H.} and Axel Krieger and Kang, {Jin U.}",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII 2022 ; Conference date: 20-02-2022 Through 24-02-2022",
year = "2022",
doi = "10.1117/12.2609103",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Israel Gannot and Israel Gannot and Katy Roodenko",
booktitle = "Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII",
}