Real-Time Dense Deformation Field Estimation of Brain Tissue from Surgical Microscope Video

The proposed project aims to develop and validate a computational method for estimating how brain tissue moves and reacts to surgical tools using only videos captured from a microscope. The student will test both classical and modern deep-learning optical-flow algorithms, adapt them to the specific challenges of microscope imaging, and evaluate how well they can capture small, localized tissue deformations. Controlled experiments on ex-vivo bovine brain specimens will be conducted to collect synchronized videos and ground-truth force measurements, allowing the team to assess the accuracy and reliability of the force-estimation model under realistic surgical conditions. The outcome of this work will be a validated pipeline that can estimate tissue forces without external sensors, enabling more objective, real-time assessment of surgical performance. This project will benefit the participating institutions by advancing low-cost, AI-driven tools for surgical simulation, strengthening collaborative research in medical image analysis, and creating new opportunities for training, knowledge transfer, and future joint publications.

Faculty Supervisor:

Houssem Gueziri

Student:

Partner:

École Nationale d'Électronique et des Télécommunications de Sfax

Discipline:

Computer science

Sector:

Artificial Intelligence; Technology

University:

Université TÉLUQ

Program: