Hervé Lombaert


Statistical Learning

Medical image analysis provides a challenge for conventional machine learning approaches due to the nature of the imaging and pathologies involved. The project aims at exploring how to better exploit statistical learning for medical image analysis.

Selected Publications

Spectral Shape Analysis

The study of complex anatomical structures requires a precise alignment of images (i.e., with a good overlap, structures in images can be compared). This alignment is, however, challenging when very large deformations exist. Instead, structures are studied using shape representations, or so called spectral signatures, that are invariant to external deformations. For example, deformed shapes have curiously similar representations in the spectral domain. Spectral signatures and representations are my current focus of research.

Selected Publications

Accurate Surface Matching

The analysis of surfaces is important in many fields, particularly in neuroscience where accuracy is critical. Spectral graph theory provides elegant solutions to the problem of surface matching.

[Fast Brain Matching with FOCUSR]

Selected Publications

Atlas Construction

Statistical atlases contain information on the average structure of organs as well as their variabilities. Their constructions require the development of shape averaging tools as well as accurate image registrations.

[Morphing between 2 Brains]

Selected Publications

Cardiac Fiber Architecture

The complex organization of the cardiac fibers plays a key role in the mechanical function and electrophysiology of the heart. Its study covers various research areas, including cardiophysiology as well as shape analysis and statistics of complex structures.

[First Human Atlas of Cardiac Fibers, visualized with MedInria]

Selected Publications

Image Segmentation

Image segmentation is a building block in image processing and analysis. In this context, fast, accurate and automatic solutions allow the processing of large quantities of images.

[4D segmentations with modified Graph Cuts, visualized with libvd]

Selected Publications

Medical Image Visualization

Visualizing and navigating through medical images are important steps for radiologists to establish diagnostics and convey their analyses to professionals.
[Virtual Endoscope inside the Heart, using Semi-automatic Segmentations]