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3 juillet 2019

PhD on Learning Statistical Human Anatomic Models

Catégorie : Doctorant

Position: A full-time doctoral research position is at INRIA – Grenoble, MORPHEO team (https://team.inria.fr/morpheo/) on learning statistical human anatomic models of shape and motion.

Date: The position will start in fall 2019. Funding is for 36 months.

Advisors: The PhD will be advised by Sergi Pujades and Edmond Boyer (Morpheo INRIA).

Date: The position will start in fall 2019. Funding is for 36 months.



Knowing the distribution of adipose, muscle, and bone tissue in the human body anatomy is crucial in the diagnosis of diseases such as type II diabetes, the planning of therapies, the guidance of the therapeutic gestures and the assessment of a therapy’s outcome.

Our current knowledge of the adipose, muscle, and bone tissues is based on the internal imaging of in-vivo patients. Examples of these imaging techniques are Computed Tomography (CT), Dual Energy X-Ray Absorption (DXA) and Magnetic Resonance Imaging (MRI). While these modalities allow accurate measurements of the inside of the body, they involve heavy and expensive equipment as well as time consuming procedures.

External dynamic measurements can be acquired with optical scanning equipment, e.g. cameras or depth sensors. These are becoming cheaper and of higher spatial and temporal resolution, allowing accurate scanning of living, moving bodies. While optically based imaging techniques provide accurate, highly dynamic (60 frames per second) reconstructions of the surface shape, they only capture the shape and appearance of the human surface.


In this project the PhD will research the relations between the observations obtained with these two modalities (internal and external scanning), and provide innovative methods in order to infer the internal measurements from the dynamic external ones. Precisely, there are two main objectives. The first objective is to create a statistical anatomic model of the human body, accounting for the distribution of adipose, muscle and bone tissue. The second objective is to develop methods to obtain a subject-specific instance of the anatomic model from external dynamic measurements of the human body.

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