Aims
Computer-aided diagnosis, prognosis and therapy from biomedical images and genomic data
Leader name(s)
Pr N. Ayache
Pr M. Barlaud
Dr H Delingette
General description
Modelling:
- biophysical models of tumour growth at various scales
- biophysical models of brain atrophy at various scales
Image Computing:
- Personalization of biophysical models from time series of medical images
- Extraction of biomarkers from images based on biophysical models
Machine Learning:
- Statistical Risk prediction
- Supervised Classification (Proximal Methods)
- Control of false positives or negatives (Neyman-Pearson method)
- Task Title
- Lung / Head & Neck Tumour growth quantification by Image Computing (micro-macro scales)
- Task Description
- Collection of images/data of patients with Lung and Head and Neck tumours
- Modelling tumour growth from micro to macroscopic scales
- Personalize tumour growth models from medical images and data to assess tumour progression and predict therapy effect
- Task Improvement
- Improve assessment of the disease progression
- Improve therapy planning (surgery & chemo- & radio-& targeted therapies)
- Reachable in 3 years
- Task Title
- Brain atrophy Quantification by Image Computing (micro-macro scales)
- Task Description
- Collection of Images/data of patients with neurodegenerative diseases
- Modelling brain atrophy from micro to macroscopic scales
- Personalize atrophy models from medical images and data for early detection of disease and to quantify the effect of therapy
- Task Improvement
- Improve early detection of degenerative diseases and quantify effect of treatment on disease progression thanks to image biomarkers
- Reachable in 2 years
- Task Title
- Machine Learning on Lung Tumour and COPD (nano)
- Task Description
- Collection of large scale Genomic data
- Statistical COPD prognosis
- Statistical Relapse Risk prediction
- Statistical Treatment response prediction
- Task Improvement
- Improve COPD prognosis, relapse risk prediction and prediction of treatment response
- Improve therapy planning
- Reachable in 5 years
- Task Title
- Modelling Head and Neck Lung Tumour (nano-micro)
- Task Description
- Collection of Genomic and images of tumour cells after drug exposure
- Estimation of cell heterogeneity (genomic and non-genomic) by time lapse imaging and drug testing
- Task Improvement
- Improve early prediction of treatment response and help for optimal drug combination design (product, concentration)
- Reachable in 1 years