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

doi:10.1117/12.2609103

2 March 2022 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

Author Affiliations +

Proceedings Volume 11953, Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII; 1195307 (2022) https://doi.org/10.1117/12.2609103
Event: SPIE BiOS, 2022, San Francisco, California, United States

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.

Conference Presentation

Citation Download Citation

Yaning Wang, Shuwen Wei, Justin D. Opfermann, Michael Kam, Hamed Saeidi, Michael H. Hsieh, Axel Krieger, and Jin U. Kang "Automated OCT A-line abdominal tissue classification using a hybrid MLP-CNN classifier during ventral hernia repair", Proc. SPIE 11953, Optical Fibers and Sensors for Medical Diagnostics, Treatment and Environmental Applications XXII, 1195307 (2 March 2022); https://doi.org/10.1117/12.2609103

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