02/06/2020
The Human Resources Strategy for Researchers

PhD contract in the field of Image processing, Machine learning financed during 3 years by the University Clermont Auvergne

This job offer has expired


  • ORGANISATION/COMPANY
    Université Clermont Auvergne
  • RESEARCH FIELD
    Computer science
  • RESEARCHER PROFILE
    First Stage Researcher (R1)
  • APPLICATION DEADLINE
    28/06/2020 00:00 - Europe/Brussels
  • LOCATION
    France › AUBIERE
  • TYPE OF CONTRACT
    Temporary
  • JOB STATUS
    Full-time
  • HOURS PER WEEK
    35 H
  • OFFER STARTING DATE
    01/10/2020
  • REFERENCE NUMBER
    UCA/ANR/003
  • IS THE JOB RELATED TO STAFF POSITION WITHIN A RESEARCH INFRASTRUCTURE?
    Yes

Subject:

Automatic segmentation of structures of the human deep brain from

nuclear magnetic resonance imaging

Supervisor: Omar Ait-Aider

Laboratory: Institut Pascal

Email and phone: omar.ait-aider@uca.fr

Co-advisor(s): JJ. Lemaire, C. Teulière

Abstract (up to 10 lines):

The objective of this thesis is to allow the identification and automatic

localization of deep brain structures from 3D MRI imagery. The challenge is

to get as close as possible to the individual architecture, taking into account

intra-class variability for more precise clinical targeting. The difficulty in

obtaining large datasets pushes us to favor unsupervised methods. We will

thus consider of proposing an original method combining the anatomical

preconceptions with the machine learning methods to obtain a fine 3D

segmentation. Data fusion methods will also be considered in order to take

advantage of the complementarity of the different types of imagery.

Skills:

• MS Degree or equivalent with major in Image processing, Machine

learning, or other related subjects

• Experience in medical imaging processing and 3D segmentation are

highly desirable

• Strong programming skills (C/C++, Python) and experience with deep

learning framework (TensorFlow/Keras/PyTorch…)

• Interest in medical applications and collaboration with clinicians

• Good spoken and written English

Keywords: 3D segmentation, Deep learning, MRI imaging, Deep brain, Data

fusion

Description (up to 1 page):

In brain MRI analysis, image segmentation is commonly used to measure and visualize the

anatomical structures of the brain, to analyze brain changes, to delineate pathological regions

and for surgical planning and image-guided interventions. In last decades, various

segmentation techniques of different precision and degree of complexity have been

developed and reported in the literature.

Recently, new methods using deep learning techniques for brain MRI segmentation have been

proposed [1-4]. They usually focus on well-known and identified structures of the brain.

The objective of the Ptolemee project, which brings together clinicians and researchers in

image analysis, is to map the deep and little-known regions of the brain. In this context, the

objective of this thesis is to develop an automatic segmentation method using a deep learning

approach based on a limited dataset of MRI images manually contoured by expert clinicians.

The dataset is necessarily limited because the manual contouring of these large and complex

MRI data is a tedious and difficult task for clinicians. The solution developed must therefore

rely on learning techniques while making the best use of more formal state-of-the-art models

in order to minimize the need for supervision.

First, the candidate will have to acquire the state of the art on brain MRI segmentation

methods. This step will be based on work currently in progress within the Institut Pascal.

In a second step the candidate will have to propose and implement original methods of

segmentation of the deep brain, taking into account the specificities of this dataset. Particular

emphasis will be placed on the combination of the a priori information provided by an

accurate atlas of the deep brain [5] and data-based deep learning approaches.

Finally, the contribution of the fusion of different imaging modalities for this segmentation

will be studied.

Références (up to ½ page):

• [1] Coupé et al., « AssemblyNet: A large ensemble of CNNs for 3D

Whole Brain MRI Segmentation », 2019

• [2] A. Guha Roy, S. Conjeti, N. Navab, and C. Wachinger, “QuickNAT: A

fully convolutional network for quick and accurate segmentation of

neuroanatomy,” NeuroImage, vol. 186, Feb. 2019.

• [3] A. de Brebisson and G. Montana, “Deep Neural Networks for

Anatomical Brain Segmentation,” in IEEE CVPR Workshops, 2015

• [4] C. Wachinger, M. Reuter, and T. Klein, “DeepNAT: Deep

convolutional neural network for segmenting neuroanatomy,”

NeuroImage, Apr. 2018.

• [5] Lemaire Jean-Jacques, De Salles Antonio, Coll Guillaume, El Ouadih

Youssef, Chaix Rémi, Coste Jérôme, Durif Franck, Makris Nikos,

Kikinis Ron, « MRI Atlas of the Human Deep Brain », in Frontiers in

Neurology vol.10, 2019

How to candidate?

Send CV, motivation letter and Master transcript to omar.ait-aider@uca.fr

and celine.teuliere@uca.fr

 
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Offer Requirements

  • REQUIRED EDUCATION LEVEL
    Other: Master Degree or equivalent

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Work location(s)
1 position(s) available at
Pascal Institute (IP)
France
Région Auvergne Rhône-Alpes
AUBIERE
63178
Campus Universitaire des Cézeaux TSA 60026 CS 60026 4, Avenue Blaise Pascal

Open, Transparent, Merit based Recruitment procedures of Researchers (OTM-R)

Know more about it at Université Clermont Auvergne

Know more about OTM-R

EURAXESS offer ID: 528414

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