Head of Team : Su Ruan et Pierre Vera

The QuantIF team's research focuses on functional and molecular medical imaging optimization and analysis : application to cancer and inflammatory pathology affecting thorax and abdomen.

Segmentation, fusion and classification methods to improve anatomical and functional medical imaging quantification and analysis.

Research fields, organ or tumor segmentation in MRI, PET/CT scan, prediction and PET/CT scan monitoring, in vivo confocal microscopy of the lung, imaging analysis and classification.

Multifunctional imaging fusion (metabolism, hypoxia, tumor proliferation) using PET/CT scan, PET/CT scan volume measurements optimization to determine the Gross Tumor Volume (GTV) for external radiotherapy and prognostic value of functional imaging in radiotherapy.

Synchronized imaging optimization (synchronized cardiac SPECT, synchronized pulmonary PET-CT).


Using learning, graph, belief, statistical and PDE methods to address segmentation, classification and fusion issues in medical imaging. Matching and identifying synergies between the medical issue and the algorithmic solution.


  • Design and approval of segmentation tools to facilitate diagnosis and radiotherapy
  • Tumor evolution prediction during and after treatments
  • Imaging analysis using fiber confocal microscopy in diagnosis assisting
  • Benefit of anatomical and functional imaging to target radiotherapy dedicated to lung tumors





Segmentation of THoracic Organs at Risk in CT images.The goal of the SegTHOR challenge is t...

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