/Hyperspectral Image Analysis for Real-time Tissue Oxygenation Measurements in Laparoscopic Surgery

Hyperspectral Image Analysis for Real-time Tissue Oxygenation Measurements in Laparoscopic Surgery

PhD - Gent | More than two weeks ago

Help surgeons save lives using cutting-edge hyperspectral imaging technology.

Hyperspectral imaging (HSI) is an emerging biomedical imaging modality, which makes use of spectral signatures to identify specific substances, such as the measurement of tissue oxygenation in a non-invasive way. The success of laparoscopic surgical procedures depends to a great extent on good tissue oxygenation. Limited tissue oxygenation can give rise to severe morbidity such as anastomotic leakage, which occurs in approximately 1 of 6 patients and carries up to 28% mortality rate. Nowadays, the existing techniques are not able to assess whole tissue oxygenation during surgery. Therefore, there is an urgent need for applying HSI technology in laparoscopic surgery to monitor tissue oxygenation and to prevent post-operative complications.

The Hyperspectral Imaging group of IMEC has vast experience in the development of HSI sensors and cameras for a wide variety of application fields (agriculture, machine vision, medicine, remote sensing, etc.). The Image Processing and Interpretation group of IMEC has been working on a number of biomedical and medical image processing and analysis applications and has a long-standing experience in the field. The Experimental Surgery Research group of Ghent University Hospital (the clinical partner) focuses on small and large animal models of colorectal and ovarian cancer and has considerable experience with the design, GCP compliant execution, and analysis of investigator-driven clinical trials with translational research endpoints.

We are looking for a motivated Ph.D. candidate who is interested in pushing the HSI technology to clinical practice. You will: (1) conduct experiments to design the optimal HSI setup for laparoscopic and in-vitro tissue oxygenation measurements (2) develop novel image processing and analysis methods to allow for efficient tissue oxygenation measurements and (3) validate the whole setup on 3-step clinical validation including blood samples, animal and patient studies (the candidate will not perform any clinical experiments himself/herself, but only use the produced images).

Required background: Computer Science (Image processing)

Type of work: 70% modeling, 20% experimental, 10% literature

Supervisor: Hiep Luong

Co-supervisor: Wilfried Philips

Daily advisor: Andy Lambrechts

The reference code for this position is 2022-113. Mention this reference code on your application form.