Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks
This work addresses the need for automated 3D reconstruction in medical imaging, specifically for laparoscopic procedures, but appears incremental as it applies a standard deep neural network to a known task.
The paper tackles the problem of automatically reconstructing 3D point sets from stereo laparoscopic images by training a six-layer deep neural network to map pixel intensities to 3D coordinates, achieving promising results on a publicly available dataset.
In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.