Introduction:
Surgical interventions for gastric procedures, such as bariatric surgery, often require precise monitoring of stomach volume changes intra-operatively to assess the effectiveness of the procedure and predict post-operative outcomes. Traditional methods for measuring stomach volume are indirect estimations, leading to limitations in accuracy and real-time monitoring. This master's thesis project aims to address these challenges by developing a novel vision-based volumetry system using intra-operative video data and pre-operative imaging information. By leveraging computer vision techniques and machine learning algorithms, this project seeks to provide surgeons with real-time feedback on stomach volume changes during surgery, contributing to improved surgical outcomes and patient care.
Objectives:
- Design and implement a vision-based system for intra-operative volumetry using a mono-camera setup.
- Develop algorithms for the automatic annotation and analysis of intra-operative video data to estimate relative volume changes in the stomach.
- Integrate pre-operative MRI data and endoscopic videos for patient-specific volumetric analysis and outcome prediction.
- Evaluate the accuracy and reliability of the developed system through experimental validation and comparison with traditional volumetric measurement methods.
Envisioned work packages:
- System Design: Design and assemble a mechatronic system for intra-operative video capture and real-time processing using a single camera, which can be tested in the lab settings.
- Vision based Algorithm Development: Develop computer vision algorithms for automatic annotation and analysis of intra-operative video frames to estimate relative volume changes in the stomach.
- Data Integration: Extract relevant information from pre-operative MRI scans, such as patient size and stomach morphology, and integrate it with intra-operative video data for patient-specific volumetric analysis.
- Experimental Validation: Conduct experiments using simulated surgical scenarios and real patient data to evaluate the accuracy and reliability of the developed system.
Expected Outcomes:
- Development of a vision-based system capable of accurately estimating relative volume changes in the stomach intra-operatively.
- Integration of pre-operative MRI data and endoscopic videos for patient-specific volumetric analysis and outcome prediction.
- Validation of the developed system's accuracy and reliability through experimental evaluation and comparison with traditional measurement methods.
- Contribution to the field of surgical robotics and computer-assisted surgery through the application of vision-based techniques for intra-operative volumetry.
- Dissemination of research findings through conference presentations and journal publications, advancing knowledge in the field of medical imaging and surgical technology.
Conclusion:
This master's thesis project aims to develop a novel vision-based system for intra-operative volumetry in gastric surgery, offering real-time feedback on stomach volume changes and contributing to improved surgical outcomes and patient care. By integrating computer vision techniques with pre-operative imaging data, this project seeks to provide surgeons with valuable insights into patient-specific volumetric changes, enhancing the precision and effectiveness of surgical interventions.
*** This MSc project is a collaboration work with Strasburg IRCAD and Strasburg IHU.