The Python notebook performs the population-wise personalization of simple geometrical models of myocardial infarct, providing as output a set of 2D synthetic images whose distribution matches the distribution of real images.
The current code is applied to the following models:
- elliptical, represented by the intersection of one ellipse with the myocardium,
- iterative spherical, represented by the union of a random number of spheres intersected with the myocardium.
Personalization is done by a learning process that optimizes the parameters of the models, with the algorithm CMA-ES (Covariance Matrix Adaptation - Evolution Strategy).
If you decide to re-use this code, please acknowledge the following publication, which presents the detailed evaluation of such personalization (choice of losses, hyperparameters, initial values):
Konik A, Clarysse P, Duchateau N. Detailed evaluation of a population-wise personalization approach to generate synthetic myocardial infarct images. Pattern Recognition Letters. 2024. In press.