Jean-Philippe Thiran

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Facial expression recognition by human observers is affected by subjective components. Indeed there is no ground truth. We have developed Discrete Choice Models (DCM) to capture the human perception of facial expressions. In a first step, the static case is treated, that is modelling perception of facial images. Image information is extracted using a computer vision tool called Active Appearance Model (AAM). DCMs attributes are based on the Facial Action Coding System (FACS), Expression Descriptive Units (E

The accurate estimation of fiber orientation distributions (fODFs) in diffusion magnetic resonance imaging (MRI) is crucial for understanding early brain development and its potential disruptions. While deep learning (DL) models have shown promise in fODF estimation from neonatal diffusion MRI (dMRI) data, the out-of-domain (OOD) performance of these models remains largely unexplored, especially under diverse domain shift scenarios. This study evaluated the robustness of three state-of-the-art DL architectu

The human auditory cortex comprises the supratemporal plane and large parts of the temporal and parietal convexities. We have investigated the relevant intrahemispheric cortico-cortical connections using in vivo DSI tractography combined with landmark-base ...

We investigate the benefits of known partial support for the recovery of joint-sparse signals and demonstrate that it is advantageous in terms of recovery performance for both rank-blind and rank-aware algorithms. We suggest extensions of several joint-spa ...

Multimodal signals can be defined in general as signals originating from the same physical source, but acquired through different devices, techniques or protocols. This applies for example to audio-visual signals, medical or satellite images. Understanding the joint dependencies of such signals is the first step toward intelligent means for their analysis. Information theory offers a rich theoretical framework in which such dependencies can be emphasized and from this, new methods of signal analysis can be

The cerebellum is involved in multiple sclerosis (MS), but its implication in early phases is still poorly understood. We investigated structural and functional cerebellar connectivity alterations in early and minimally impaired MS patients, and their corr ...

A system for user-guided audio source separation is presented in this article. Following previous works on time-frequency music representations, the proposed User Interface allows the user to select the desired audio source, by means of the assumed fundame ...

In this paper we present an efficient numerical scheme for the recently introduced Geodesic Active Fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, data-term and regularization term are combined trough multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic spatially varying and data-dependent tuning of the regularization strength

We present a 3D geometric flow designed to evolve in Diffusion Tensor Magnetic Resonance Images(DT-MRI) along fiber tracts by measuring the diffusive similarity between voxels. Therefore we define a front propagation speed that is proportional to the similarity between the tensors lying on the surface and its neighbor in the propagation direction. The method is based on the assumption that successive voxels in a tract have similar diffusion properties. The front propagation is implemented using level set me

The use of higher order autocorrelations as features for pattern classification has been usually restricted to second or third orders due to high computational costs. Since the autocorrelation space is a high dimensional space we are interested in reducing the dimensionality of feature vectors for the benefit of the pattern classification task. An established technique is Principal Component Analysis (PCA) which, however, cannot be applied directly in the autocorrelation space. In this paper we develop a ne

Volumetric ultrasound (US) is a very promising development of medical US imaging. An under-exploited advantage of volumetric US is the mitigation of the strict probe positioning constrains necessary to acquire 2D scans, potentially allowing the decoupling of US image acquisition and diagnosis. However, today’s 3D US systems are large and beset by high power and cost requirements, making them only available in well-equipped hospitals. In this work [1], [2], we propose the first telesonography-capable medical

Ultrafast ultrasound imaging enables imaging at kilohertz frame rates, at the cost of a degraded image quality in terms of contrast and resolution. To reach an image quality comparable to focused imaging, the standard technique is coherent compounding, which requires a high number of transmissions and reduces the effective frame rate. In this paper, we introduce a COnvolutional Compounding Algorithm (COCA), a non-linear compounding method that aims at improving the quality of plane-wave imaging by virtually

Despite well-established baselines, learning of scene depth and ego-motion from monocular video remains an ongoing challenge, specifically when handling scaling ambiguity issues and depth inconsistencies in image sequences. Much prior work uses either a su ...

We propose an information theoretic model that unifies a wide range of existing information theoretic signal processing algorithms in a compact mathematical framework. It is mainly based on stochastic processes, Markov chains and error probabilities. The p ...

In this paper, we advance the state of the art in variational image segmentation through the fusion of bottom-up segmentation and top-down classification of object behavior over an image sequence. Such an approach is beneficial for both tasks and is carried out through a joint optimization, which enables the two tasks to cooperate, such that knowledge relevant to each can aid in the resolution of the other, thus enhancing the final result. In particular, classification offers dynamic probabilistic priors to

Normal course of cortical maturation has been evidenced for many years through gray matter volume measurements and abnormal gray matter volumes have frequently been reported as a part of a pathological brain development in neurogenetic or psychiatric disorders. The pathogenic processes underlying abnormal gray matter volume are not yet well understood, as either altered cortical thickness or a defect in cortical expansion may decrease cortical volume. Resolving this issue may provide clues for localizing th

Clustering algorithms have evolved to handle more and more complex structures. However, measures allowing to qualify the quality of such partitions are rare and only specic to certain algorithms. In this work, we propose a new cluster validity measure (CVM) handling solutions with arbitrary shapes and various levels of outlier rejection based on notions of cluster cores and outliers. Moreover, we propose an adequate cluster merging system (CMS) to group cluster cores sharing some of their outliers. These ou

From toddler to late teenager, the macroscopic pattern of axonal projections in the human brain remains largely unchanged while undergoing dramatic functional modifications that lead to network refinement. These functional modifications are mediated by inc ...

Purpose To investigate the impact of head motion on the performance of subject‐specific dynamic parallel transmit (pTx) pulses and the resulting image quality of structural neuroimaging sequences at 7 T. Methods B 0 and multichannel B 1 + maps were acquired in 11 healthy subjects across 15 head positions. For each head position, small‐tip‐angle k T ‐spoke pulses and scalable nonparametrized pulses were optimized with different field maps following two experimental conditions: (1) No recalibration (NR): maps

Computer-aided clinical decision support tools for radiology often suffer from poor generalizability in multi-centric frameworks due to data heterogeneity. In particular, magnetic resonance images depend on a large number of acquisition protocol parameters ...

Jean-Philippe Thiran

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Capturing Human Perception of Facial Expressions by Discrete Choice Modelling

Domain shifts in infant brain diffusion MRI: Quantification and mitigation for fODF estimation

Intrahemispheric cortico-cortical connections of the human auditory cortex

Joint Sparsity with Partially Known Support and Application to Ultrasound Imaging

An information theoretic perspective on multimodal signal processing

Cerebellar Connectomics Provide New Biomarkers in Early Multiple Sclerosis

Musical Audio Source Separation Based on User-Selected F0 Track

Fast Geodesic Active Fields for Image Registration based on Splitting and Augmented Lagrangian Approaches

White Matter Mapping in DT-MRI Using Geometric Flows

PCA in Autocorrelation Space

Live Demonstration: Inexpensive 1024-Channel 3D Telesonography System on FPGA

Sparse convolutional plane-wave compounding for ultrasound imaging

SynDeMo: Synergistic Deep Feature Alignment for Joint Learning of Depth and Ego-Motion

From Error Probability to Information Theoretic (Multi-Modal) Signal Processing

Simultaneous Segmentation and Object Behavior Classification of Image Sequences

Determinants of cortical gray matter volume: hypothesis based on developmental cohorts with normal and abnormal cortical morphology

Cluster Validity Measure and Merging System for Hierarchical Clustering considering Outliers

White matter maturation reshapes structural connectivity in the late developing human brain

How Much Does Motion Matter? Evaluating the Motion Robustness of pTx Pulses at 7 T

Neuroimaging Harmonization Using cGANs: Image Similarity Metrics Poorly Predict Cross-Protocol Volumetric Consistency