RAFFAELLI , Charles

Machine learning applications in ultrasound imaging are limited by access to ground-truth expert annotations, especially in specialized applications such as thyroid nodule evaluation. Active learning strategies seek to alleviate this concern by making more effective use of expert annotations; however, many proposed techniques do not adapt well...

Landmark detection in medical images is important for many clinical applications. Learning-based landmark detection is successful at solving some problems but it usually requires a large number of annotated datasets for the training stage. In addition, traditional methodsusually fail for the landmark detection of fine objects. In this paper, we...

To ensure appropriate blood supply through the brain, the cerebral blood vessels, neurons, and as-trocyte glial cells must interact according to complex mechanisms. This interaction, named "neurovascular coupling" (NVC), describes the link between neuronal activity and cerebral blood flow (CBF) changes. 25 Several methods have been developed to...

To assess the quality of insertion of Cochlear Implants (CI) after surgery, it is important to analyze the positions of the electrodes with respect to the cochlea based on post-operative CT imaging. Yet, these images suffer from metal artifacts which often entail a difficulty to make any analysis. In this work, we propose a 3D metal artifact...

The aim of this study is to define an automated and reproducible framework for cochlear anatomical analysis from high-resolution segmented images and to provide a comprehensive and objective shape variability study suitable for cochlear implant design and surgery planning. For the scala tympani (ST), the scala vestibuli (SV) and the whole...

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on...

Metal Artifacts creates often difficulties for a high-quality visual assessment of post-operative imaging in computed tomography (CT). A vast body of methods have been proposed to tackle this issue, but these methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of...

Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric spatial transformations applied to reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images...

International audience

International audience

Background: Excess visceral fat (VF) or high body mass index (BMI) is risk factors for severe COVID-19. The receptor for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is expressed at higher levels in the VF than in the subcutaneous fat (SCF) of obese patients.Aim: To show that visceral fat accumulation better predicts severity of...

Main goalImprove the understanding of human temporal bone computed tomography (CT) scans based on semi-automatically segmented microcomputed tomography (µCT).IntroductionThe three-dimensional ear anatomy is complex and challenging to interpret in CT scans because small structures are partially visible. Histological slices provide complementary...

Understanding cochlear anatomy is crucial for developing less traumatic electrode arrays and insertion guidance for cochlear implantation. The human cochlea shows considerable variability in size and morphology. This study analyses 1000+ clinical temporal bone CT images using a web-based image analysis tool. Cochlear size and shape parameters...