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A deep learning approach to surface reconstruction for surgical navigation during laparoscopic, endoscopic or robotic surgery

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2019
Zabardast, Ami
Minimally invasive surgical procedures utilize technology to provide surgeons with more functionality as well as a better perspective to help them succeed in their tasks and reduce operations risks. Surgeons usually rely on screens and cameras during minimally invasive surgeries such as Laparoscopic, Endoscopic, or Robotic Surgeries. Currently, operating rooms use information from different modalities such as Computer-Aided Tomography and Magnetic Resonance Imaging. However, the information is not integrated, and the task of extracting and combining features falls under the surgeon’s expertise. Conventional cameras, although very helpful, are not capable of transmitting every aspect of the scene including depth perception. Recently stereo cameras are being introduced to operating rooms. Utilizing stereo endoscopic equipment alongside algorithms to process the information can enable depth perception.The process of extracting depth information from stereo cameras, also known as Stereo Correspondence, is still an active research field in computer science. Understanding depth information from the view is a necessary step for reconstruction of the scene in a 3D environment. Ultimately, this reconstructed environment acts as a basis to build an Augmented Reality with extra information baked into the scene to help the surgeon. Artificial Neural Networks (ANNs), specially Convolutional Neural Networks (CNNs), have revolutionized the computer vision research in the past few years. One of the problems that researchers tried to solve using ANNs was Stereo Correspondence. There are variations of CNNs with excellent accuracy in Stereo Correspondence problem. This thesis aims to achieve surface reconstruction from in vitro stereo images of organs using Deep Neural Networks and in silico simulations.