Modelling of Surgical Workflows

Minimally invasive interventions are becoming increasingly common as they improve patient outcomes such as recovery time and infection risk; however,

these procedures can be difficult for operators to learn. To improve surgical training efficacy, computer-assisted training, which must provide feedback in the form of instruction and skill evaluation, can be used. The training system must have a sufficient model of the procedural workflow, which can be described surgical process models. These surgical process models, combined with methods for automatically determining the current phase of an intervention can be used to automate instruction. For this project, I propose investigating surgical process modelling for the technical subcomponents of needle-based interventions. Subsequently, these surgical process models can be combined with a previously developed workflow segmentation algorithm to provide automatic instruction. The resulting system will be integrated into the opensource PerkTutor platform ( for image-guided intervention training.

Faculty Supervisor:

Gabor Fichtinger


Matthew Holden



Computer science



Queen's University



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