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

Download

index.pdf

Date

2019

Author

Zabardast, Ami

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

230
views

307
downloads

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.

Subject Keywords

Robotics in medicine., Convolutional Neural Network, Surface Reconstruction, Stereo Imaging, Surgical Navigation, Augmented Reality.

URI

http://etd.lib.metu.edu.tr/upload/12623673/index.pdf
https://hdl.handle.net/11511/43928

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

A Semantically Enriched Clinical Guideline Model Enabling Deployment in Heterogeneous Healthcare Environments Laleci, Gokce B.; Doğaç, Asuman (Institute of Electrical and Electronics Engineers (IEEE), 2009-03-01) Clinical guidelines are developed to assist healthcare practitioners to make decisions on patient's medical problems, and as such they communicate with external applications to retrieve patient data to initiate medical actions through clinical workflows, and transmit information to alert/reminder systems. The interoperability problems in the healthcare information technology domain prevent wider deployment of clinical guidelines because each deployment requires a tedious custom adaptation phase. In this pap...
Automatic Bayesian segmentation of human facial tissue using 3D MR-CT fusion by incorporating models of measurement blurring, noise and partial volume Şener, Emre; Kanoğlu, Utku; Mumcuoğlu, Ünal Erkan; Department of Engineering Sciences (2012) Segmentation of human head on medical images is an important process in a wide array of applications such as diagnosis, facial surgery planning, prosthesis design, and forensic identification. In this study, a new Bayesian method for segmentation of facial tissues is presented. Segmentation classes include muscle, bone, fat, air and skin. The method incorporates a model to account for image blurring during data acquisition, a prior helping to reduce noise as well as a partial volume model. Regularization ba...
A hybrid assessment approach for medical device software development companies Özcan Top, Özden (2018-07-01) Medical device software development organizations are bound by regulatory requirements and constraints to ensure that developed medical devices will not harm patients. Medical devices have to be treated as complete systems and be evaluated in this manner. Instead of manufacturers having to ensure compliance to various regulatory standards individually, the authors previously developed a medical device software process assessment framework called MDevSPICE((R)) that integrates the regulatory requirements fro...
A Case for Societal Digital Security Culture BEN OTHMANE, Lotfi; WEFFERS, Harold; RANCHAL, Rohit; Angın, Pelin; BHARGAVA, Bharat; MOHAMAD, Mohd M (2013-07-10) Information and communication technology systems, such as remote health care monitoring and smart mobility applications, have become indispensable parts of our lives. Security vulnerabilities in these systems could cause financial losses, privacy/safety compromises, and operational interruptions. This paper demonstrates through examples, that technical security solutions for these information systems, alone, are not sufficient to protect individuals and their assets from attacks. It proposes to complement (...
A Secure Semantic Interoperability Infrastructure for Inter-Enterprise Sharing of Electronic Healthcare Records Boniface, Mike; Watkins, E. Rowland; Saleh, Ahmed; Doğaç, Asuman; Eichelberg, Marco (2006-06-09) Healthcare professionals need access to accurate and complete healthcare records for effective assessment, diagnosis and treatment of patients. The non-interoperability of healthcare information systems means that inter-enterprise access to a patient's history over many distributed encounters is difficult to achieve. The ARTEMIS project has developed a secure semantic web service infrastructure for the interoperability of healthcare information systems. Healthcare professionals share services and medical in...

Citation Formats

A. Zabardast, “A deep learning approach to surface reconstruction for surgical navigation during laparoscopic, endoscopic or robotic surgery,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Medical Informatics., Middle East Technical University, 2019.