Ph.D Position: Learning Statistical Human Anatomic Models of Shape and Motion

Position:  A full-time doctoral research position is open at INRIA – Grenoble, MORPHEO team (https://team.inria.fr/morpheo/).

Date: The position will start in fall 2021. 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 2021. Funding is for 36 months.

Context:

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.

 

Objectives

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.

 

Scientific Approach:
To reach the objectives a Data-Driven strategy will be used.

The first step will be to register the different datasets (containing MRI – X-Ray and optical surface scans) into a common – anatomically plausible – representation.

Once the datasets registered, statistical models will be learned. Linear (PCA) as well as non-linear (VAE) models, among others, will be studied. Different data representations (meshes, volumetric grids, implicit surfaces, …) will also be investigated in order to best represent and model the data.

The last step of the PhD will leverage the learned statistical models, in order to infer the internal anatomic structures (a subject-specific instance of the anatomic model) from external dynamic measurements.

Data corpus:

Several datasets – publicly available as well as internally acquired datasets – will be used during the project. No data acquisition will be performed by the PhD.

Candidate Profile:

– A master in Computer Science or Applied Mathematic (mandatory).
– Strong mathematical background – geometry – physics – optimization techniques
– Strong coding skills (c++ / python)
– Good Oral and written English
– Candidate should have preliminary experience in at least two of the following areas: image processing – geometry processing – physics simulation – temporal series – machine learning. A specific section in the application letter must explain the personal experience in these areas.

 

How to apply:

Please send your application including

  • Mandatory: Complete CV
  • Mandatory: Letter of motivation (at most one page) – briefly describing the personal experience in the relevant areas (see Candidate Profile).
  • Mandatory: Degrees and lists of grades (translated to English or French)
  • Mandatory: Name and e-mail address of two references (this typically includes your Master thesis supervisor)
  • Topic of Master thesis and report if available

through this Jobin website: https://recrutement.inria.fr/public/classic/fr/offres/2021-03783

NOTE: only complete applications submitted through Jobin will be considered.

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