Supervisor: Giacomo Cappon
Co-supervisor: Ali Gharaviri, Edoardo Bori
Co-supervisor Department/Company: Heart Rhythm Research Brussels, UZ Brussel, ECAM Brussel
Creation Date: 06/02/2026 10:12
The thesis project aims to develope AI-based deep learning model for medical image
segmentation, with a primary focus on obtaining highly accurate cardiac atrial anatomy
for advanced clinical applications. The work involves refining architectures such as U-
Net and transformer-based networks through improved training strategies, larger
datasets, and organ-specific optimisation. The resulting segmented data will support
two main objectives: the creation of realistic 3D-printed organ models for surgical
training and the generation of patient-specific heart surface reconstructions for high-
resolution cardiac mapping.
Dataset type: Already acquired data
Dataset description: 3D Late Gadolinium-Enhanced MRIs and related ground truth segmentation masks
List of Methods: Deep learning, convolutional neural network, computational infrastructure, Linux, model training, testing, and evaluation
Deep learning