Fringe projection profilometry (FPP) is being developed as a 3D vision system to assist robotic surgery and autonomous suturing. Conventionally, fluorescence markers are placed on a target tissue to indicate suturing landmarks, which not only increase the system complexity, but also impose safety concerns. To address these problems, we propose a numerical landmark detection algorithm based on deep learning. A landmark heatmap is regressed using an adopted U-Net from the four channel data generated by the FPP. A Markov random field leveraging the structure prior is developed to search the correct set of landmarks from the heatmap. The accuracy of the proposed method is verified through ex-vivo porcine intestine landmark detection experiments.
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.
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