StaSiS-Net: a stacked and siamese stereo network for depth reconstruction in modern 3D laparoscopy.

Accurate and real-time methodologies for a non-invasive three-dimensional representa-tion and reconstruction of internal patient structures is one of the main research fieldsin computer-assisted surgery and endoscopy. Mono and stereo endoscopic images ofsoft tissues are converted into a three-dimensional representation by the estimation ofdepth maps. However, automatic, detailed, accurate and robust depth map estimationis a challenging problem which, moreover, is strictly dependent on a robust estimateof the disparity map. Many traditional algorithms are often inefficient or not accu-rate. In this work, novel self-supervised stacked and Siamese encoder/decoder neuralnetworks are proposed to compute accurate disparity maps for 3D laparoscopy depthreconstructions. These networks produce disparities in real-time on standard GPU-equipped desktop computers and after, with a minimal parameter configuration theirdepth reconstruction. We compare their performance on three different public datasetsand on a new challenging simulated dataset and they outperform state-of-the-art monoand stereo depth estimation methods. Extensive robustness and sensitivity analyses onmore than 30 000 frames has been performed. This work leads to important improve-ments in mono and stereo real-time depth estimations of soft tissues and organs with avery low average mean absolute disparity reconstruction error with respect to groundtruth.