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.