Jean-Philippe Thiran

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The main assumption of fiber-tracking algorithms is that fiber trajectories are represented by paths of highest diffusion, which is usually accomplished by following the principal diffusion directions in every voxel. The state-of-the-art approaches known a ...

Accurate measurement of knee kinematics during functional activities suffers mainly from soft tissue artifact (STA): the combination of local surface deformations and rigid movement of markers relative to the underlying bone (also called rigid STA movement ...

Quantitative MRI allows establishing normative atlases of relaxometry parameters which enable single-subject comparisons for anomaly detection. The large anatomical inter-subject variability of the brain cortex and its convoluted shape, however, complicate such comparison in this region. In this study, a method to align inter-subject brain cortices is proposed and a voxel-wise normative T1 atlas in the cortex is built from a cohort of 133 healthy subjects scanned at 7T. The atlas is used to detect and chara

We introduce a new approach for finger-spelling recognition from video sequences, relying on the collaboration between the feature extraction and behavior inference processes. The inference process dynamically guides the segmentation- based feature extract ...

This paper addresses the issue of automatic classification of the six universal emotional categories (joy, surprise, fear, anger, disgust, sadness) in the case of static images. Appearance parameters are extracted by an active appearance model(AAM) representing the input for the classifi- cation step. We introduce Relevant Component Analysis (RCA) in the context of facial expression recognition framework and we test this method against several other classi- fication techniques, including LDA, GDA and SVM, o

Curvature Gabor features have recently been shown to be powerful facial texture descriptors with applications on face recognition. In this paper we introduce their use in facial action unit (AU) detection within a novel framework that combines multiple Loc ...

Conventional ultrasound (US) image reconstruction methods rely on delay-and-sum (DAS) beamforming, which is a relatively poor solution of the image reconstruction problem. An alternative to DAS consists in using iterative techniques which require both an a ...

Multi-center neuroimaging studies have more power than smaller ones to conduct sophisticated studies of basic neuroanatomy and clinical disorders. One important confound of combining images obtained from different scanners is the potential for scanner effe ...

A new sampling theorem on the sphere has been developed recently, reducing the number of samples required to represent a band-limited signal by a factor of two for equiangular sampling schemes. For signals sparse in a spatially localised measure, such as i ...

Background: 3D reconstruction of Wilms’ tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate th ...

Recent studies demonstrate promising efficacy with immune checkpoint inhibitors (ICI) for brain metastases (BM), an unmet need in modern oncology. However, a predictive biomarker for ICI efficacy is needed to inform precision-based use of ICI given its hig ...

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. Thi ...

In ultrasound (US) imaging, beamforming is usually separated from the deconvolution or some other post-processing techniques. The former processes raw data to build radio-frequency (RF) images while the latter restore high-resolution images, denoted as tis ...

The use of higher-order local autocorrelations as features forpattern recognition has been acknowledge since many years, but their applicability was restricted to relatively low orders (2 or 3) and small local neighborhoods, due to combinatorial increase in computational costs. In this paper a new method for using these features is presented, which allows the use of autocorrelations of any order and of larger neighborhoods. The method is closely related to the classifier used, a Support Vector Machine (

In this paper, we propose an energy functional to segment objects whose global shape is

A sparse classifier is guaranteed to generalize better than a denser one, given they perform identical on the training set. However, methods like Support Vector Machine, even if they produce relatively sparse models, are known to scale linearly as the number of training examples increases. A recent proposed method, the Kernel Matching Pursuit, presents a number of advantages over the

Jean-Philippe Thiran

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Anatomical Priors to improve Global Tractography

Soft tissue artifact assessment during treadmill walking in subjects with total knee arthroplasty

A normative cortical T1 atlas for single-subject pathology detection at 7T

Finger-spelling Recognition within a Collaborative Segmentation/Behavior Inference Framework

Ultrasound Measurement of the Fibrous Cap in Symptomatic and Asymptomatic Atheromatous Carotid Plaques

Relevant Component Analysis for static facial expression classification

Multiple Local Curvature Gabor Binary Patterns for Facial Action Recognition

Ultrafast ultrasound imaging as an inverse problem: Matrix-free sparse image reconstruction

Testing the variability of Diffusion Spectrum Imaging (DSI): Inter- and intra-site comparison on "identical" 3T scanners

Sparse image reconstruction on the sphere: implications of a new sampling theorem

Divide-and-Rule: Self- Supervised Learning for Survival Analysis in Colorectal Cancer

Representing Diffusion MRI in 5D Simplifies Regularization and Segmentation of White Matter Tracts

Study protocol for a two-site clinical trial to validate a smartphone-based artificial intelligence classifier identifying cervical precancer and cancer in HPV-positive women in Cameroon

Automation of Wilms’ tumor segmentation by artificial intelligence

Multimodal Deep Learning-Based Prediction of Immune Checkpoint Inhibitor Efficacy in Brain Metastases

JULIDE: a software tool for 3D reconstruction and statistical analysis of autoradiographic mouse brain sections

Beamforming-deconvolution: A novel concept of deconvolution for ultrasound imaging

Higher Order Autocorrelations for Pattern Classification

A Priori Information in Image Segmentation: Energy Functional based on Shape Statistical Model and Image Information

Adaptive Kernel Matching Pursuit for Pattern Classification