Internally developed software


    A finite difference method based software for the numerical assessment of clinical IRE.

    IRENA is a C++ software which provides a numerical tool to assess the distribution of the electric field and check whether the tumor is included in the estimated treatment area or not. For this purpose, numerical simulations are performed from the real clinical data: the medical images which give the position of the organ and the tumor, the real position of the needles, and the current graphs of test pulses for tissues conductivity calibration.

  • Cadmos

    A library (written in C++11) gathering routines to solve various types of partial differential equations (diffusion, advection, level-set) with finite-volumes methods on a cartesian grids (as medical images).

    The main purpose of it is to have a powerful tool (3D schemes, numerical schemes implemented, HPC) albeit extensible versatile and simple (using an object-oriented approach) to develop for. This allows us to focus our development on numerical methods on only one source. The choices made through its development are aimed at answering all the challenges of modern scientific computing: accuracy, simplicity and flexibility. Hence Cadmos is meant to be an abstraction layer hiding implementation details of the discretization of the equations composing a model. In this sense, for the user, it is a Domain Specific Langage for writing mathematical model based on PDEs. As data assimilation of medical images is also a large part of our work, Cadmos also contains routines to help solve inverse problems by stochastic algorithms.

    Snapshot of a 3D simulation of a metastasis growing in a lung.

    Snapshot of a 3D simulation of a metastasis growing in a lung. The code was built on the Cadmos framework by J. Jouganous.


    A Matlab library for simulation and calibration of mechanistic models of metastatic development. Data used for calibration consist of longitudinal measurements of primary tumor size and total metastatic burden (e.g. measured by bioluminescence techniques) or metastatic size distribution from anatomical imaging techniques (CT scans or MRI). Parameter estimation is performed either by an individual approach or at the population level using nonlinear mixed-effects modeling.


    Computer-Assisted Research about Cancer growth and INsights on Oncological Mechanisms

    A software for nonlinear regression of tumor growth models and statistical inference.

    This software is primarily designed to perform a modeling analysis of tumor growth kinetics. Given a data set of longitudinal measurements of tumor size in a population, it fits several models of tumor growth, computes several goodness-of-fit statistical metrics, identifies the parameters of the models and estimates the uncertainty associated to their determination. It provides several graphical and numerical outputs (in the form of LaTeX tables).

  • PapriK

    A Python library gathering routines to easily process medical images and contours.

Comments are closed.