There are research opportunities for undergraduate students, who are interested in working in medical image analysis. Please contact me with your CV.
There are also two Ph.D. positions available in the GI AI Lab in Fall 2023 at the University of Guelph, Canada. You are also highly encouraged to apply for NSERC Research Awards.
The projects will involve developing advanced image segmentation methods based on latest machine learning technologies to enable (semi-) automated identification of challenging 3-dimensional anatomic structures from images with low contrast and/or spatial resolution. Image analysis methods will be developed to identify cardiac tissues that are abnormal or at-risk of progressing from sub-clinical to symptomatic disease states. We are also exploring the use of machine learning techniques (e.g., convolution neural networks) for image segmentation and registration. These projects will be in collaboration with our partners and collaborators.
For top Candidates, I am happy to nominate to the Ontario Graduate Scholarship (OGS), where more information can be found through the link below:
Qualifications: The ideal candidate will have a master’s degree in, Engineering, Computer Science, Medical Physics, or related field with experience in medical image processing. Familiarity and past experience with 3D Slicer, python and/or VTK/ITK programming would be an asset.
The prospective graduate students have the opportunity to participate in the Guelph School of Engineering. The students have the opportunity to participate in cutting-edge and multi-disciplinary research projects involving development and evaluation of image analysis methodologies. Please refer to the following profiles for more information on my research interests, and contact me if you are interested.
Cardiac Image Analysis
Currently, most widely used image-based biomarkers of cardiac structure and function have been limited to global indices, such as infarct mass, left ventricular (LV) mass, end-diastolic volume, and ejection fraction. With recent advancements in MRI technology to acquire high-resolution three-dimensional (3D) images of the heart and more accurate and quantitative techniques to image the infarct structure, there is a significant opportunity for realizing more sensitive and more localized measurements of cardiac structure and function. One of the main challenges to properly evaluate and to eventually translate this imaging technique for analysis of structure and function of the heart, is the lack of robust automated image analysis methods. The following is an image processing pipeline for building and simulating electrophysiological models of the heart.
Digital Histopathology Image Analysis
Hirschsprung’s disease is a critical medical condition that affects the large intestine and causes problems with passing stool in small children. The condition is present at birth as a result of missing nerve cells in the muscles of the child’s colon. Surgery to bypass the part of the colon that has no nerve cells is a major treatment for Hirschsprung’s disease. The decision for this surgery is based on qualitative inspection of density of nerve cells in the digital histopathology images of excised specimens. However, even leading clinical experts cannot avoid human errors which can lead to catastrophic results. Therefore, developing an automated and quantitative approach for processing of digital histopathology images of patients could enormously increase the accuracy of decisions made by clinicians.