Master MVA 2023-2024 First Semester - Medical Image Analysis based on generative, geometric and biophysical models

Hervé Delingette
Equipe Epione
2004, route des Lucioles B.P. 93
06902 Sophia Antipolis Cedex
https://team.inria.fr/epione/en/
04 92 38 77 64
Herve.Delingette@inria.fr

Xavier Pennec
Equipe Epione
2004, route des Lucioles B.P. 93
06902 Sophia Antipolis Cedex
https://team.inria.fr/epione/en/
04 92 38 76 64
xavier.pennec@inria.fr

Course Objectives

The course is an advanced introduction to the mathematical and computing tools used in medical image analysis, ranging from generic image analysis such as image registration and image segmentation to more advanced techniques in statistical and mechanical modeling for computational anatomy and physiology.

Classes are given on Tuesday afternoons from 14:00 to 17:15 at ENS Saclay

The examination consists in the group study of a research article with a report and a 10 min presentation and a multiple choice questionnaire

Liste of courses, slides, course notes and additional material (updated in real-time)

• Tuesday Oct 3, 2023, 14:00-17:15 (ENS Saclay, salle 2E30) Introduction to Medical Image Acquisition, Image Filtering [Hervé Delingette]
  • Video du cours de 2020 (in French)
  • Slides
  • GitLab for all notebooks
• Tuesday Oct 10, 2023, 14:00-17:15 (ENS Saclay, salle 3E34) Medical Image Registration [Xavier Pennec]
  
  • Slides on image registration
  No video recording for this course. You may look at the second part (starting at 1:26) of the video of the introduction course above for a condensed version.
• Tuesday Oct 17, 2023, 14:00-17:15 (ENS Saclay salle 2E30) Riemanian Geometry and Statistics [Xavier Pennec]
• Slides.
• Course notes: Introduction to differential and Riemannian Geometry [To find (much) more information on Lie groups and Riemannian manifolds, I recommend the book of Jean Gallier Notes on Differential Geometry and Lie Groups]
• Course notes: The example of rotations
• Course notes: Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements [Another presentation by Tom Fletcher: Statistics on manifolds]
• Tuesday Oct 23, 2024: 14:00-17:15 (ENS Saclay, salle 1B18, through Visioconferencing) Basis of Image Segmentation [Hervé Delingette]
  • Slides on Medical image Filtering
  • Slides on Medical image Classification
  • The video of the course of Oct 23rd 2023 (in English)
• Tuesday Nov 7, 2023: 14:00-17:15 (ENS Saclay, salle 2E30) Image Segmentation based on Clustering and Markov Random Fields [Hervé Delingette]
  • Lecture Notes on ELBO, EM and Gaussian Mixtures
  • Slides on Medical image Classification & Markov Random Fields
  • Course video
  • Notebook for Gaussian Mixture Models for Brain Segmentation
  • Notebook for K-means for Brain Segmentation
  • Notebook for Gaussian Mixture Models for Brain Segmentation with bias field correction
• Tuesday Nov 14, 2023, 14:00-17:15 (ENS Saclay, salle 3E34) Shape constrained image segmentation [Hervé Delingette]
• Slides on Medical image Classification & Markov Random Fields
• Course video
• GrabCut Segmentation
• Random Walker Segmentation
• Tuesday Nov 21th : 14:00-17:15 (ENS Saclay, salle 2E30) Analysis in the space of Covariance Matrices [Xavier Pennec]
  • Slides
  • Video du cours de 2020 (in French)
  • Course notes: Manifold valued image processing with SPD matrices
• Tuesday Nov 28th : 14:00-17:15 (ENS Saclay, salle 1B18) Diffeomorphic Registration and Computational Anatomy [Xavier Pennec]
  • Video of the course (in English)
  • Slides
  • Course notes: Beyond Riemannian Geometry: The affine connection setting for transformation groups
• Tuesday Dec 5th, 2023, 14:00-17:15 (Visio) Exam [Hervé Delingette, Xavier Pennec].
  • Zoom link : https://univ-cotedazur.zoom.us/j/88034381090?pwd=cllFTzNxMFhzWUYxaExTRUtBU3IrUT09 ID de rŽunionÊ: 880 3438 109 Code secretÊ: 255431. Phone number : +33 1 8699 5831 Then enter ID followed by # then code followed by #

Resources

• Online lectures and courses at College de France

Articles for the research project (last update oct 30th, 192Mb)

Choice of research paper

• G1 Laura Fuentes et Tania Aguirre : "Efficient globally optimal segmentation of cells in fluorescence microscopy images using level sets and convex energy functionals"
• G2 Sahbi Hanafi et Yona Uzan : "A deep-learning approach for direct whole-heart mesh reconstruction"
• G3 Louis Tocquec et Théo Danielou : "Piecewise-diffeomorphic image registration: Application to the motion estimation between 3D CT lung images with sliding conditions"
• G4 Vincent Herfeld et Alexandre Cahill : "Population Shape Regression from Random Design Data" Brad C. Davis á P. Thomas Fletcher á Elizabeth Bullitt á Sarang Joshi
• G5 Mahmoud Hegazy et Ines Vati : "Boundary loss for highly unbalanced segmentation"
• G6 Victor Struillou : "Biomechanisms for modelling cerebral cortical folding"
• G7 Théotime de Charrin, et Mathis Reymond : "Modeling the Shape of the Brain Connectome via Deep Neural Networks"
• G8 Grégoire Gissot et Florencia Boccarato : "SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining"
• G9 Martin Brosset et Mathilde Dupouy : "Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation" Krishna Chaitanya et al.