Encoding the Latent Posterior of Bayesian Neural Networks for Uncertainty Quantification.

Bayesian Neural Networks (BNNs) have long been considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. While they could capture more accurately the posterior distribution of the network parameters, most BNN approaches are either limited to small networks or rely on constraining assumptions, e.g.

Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: Assessment, comparison and improvement.

Renal tubular structures, such as ureters, arteries and veins, are very important for building a complete digital 3D anatomical model of a patient. However, they can be challenging to segment from ceCT images due to their elongated shape, diameter variation and intra- and inter-patient contrast heterogeneity. This task is even more difficult in pediatric and pathological subjects, due to high inter-subject anatomical variations, potential presence of tumors, small volume of these structures compared to the surrounding, and small available labeled datasets.

Super-resolution in brain positron emission tomography using a real-time motion capture system.

Super-resolution (SR) is a methodology that seeks to improve image resolution by exploiting the increased spatial sampling information obtained from multiple acquisitions of the same target with accurately known sub-resolution shifts. This work aims to develop and evaluate an SR estimation framework for brain positron emission tomography (PET), taking advantage of a high-resolution infra-red tracking camera to measure shifts precisely and continuously. Moving phantoms and non-human primate (NHP) experiments were performed on a GE Discovery MI PET/CT scanner (GE Healthcare) using an NDI Polaris Vega (Northern Digital Inc), an external optical motion tracking device.

Manifold Learning via Linear Tangent Space Alignment (LTSA) for Accelerated Dynamic MRI With Sparse Sampling.

The spatial resolution and temporal frame-rate of dynamic magnetic resonance imaging (MRI) can be improved by reconstructing images from sparsely sampled k -space data with mathematical modeling of the underlying spatiotemporal signals. These models include sparsity models, linear subspace models, and non-linear manifold models. This work presents a novel linear tangent space alignment (LTSA) model-based framework that exploits the intrinsic low-dimensional manifold structure of dynamic images for accelerated dynamic MRI.

Instruments Segmentation in X-ray Fluoroscopic Images for Endoscopic Retrograde Cholangio Pancreatography.

In this work, we propose a method to segment endoscope and guidewire from 2D X-ray fluoroscopic images of an endoscopic retrograde cholangiopancreatography (ERCP). We used an improved U-Net model. We obtained a Dice score of 0.

Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier.

While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels). We create a dataset that simulates pap smears regions and uses a loss, we call classification under regression constraint, to train an efficient region classifier (about 66.

Motion correction for PET data using subspace-based real-time MR imaging in simultaneous PET/MR.

Image quality of positron emission tomography (PET) reconstructions is degraded by subject motion occurring during the acquisition. Magnetic resonance (MR)-based motion correction approaches have been studied for PET/MR scanners and have been successful at capturing regular motion patterns, when used in conjunction with surrogate signals (e.g.

Automated brain MRI metrics in the EPIRMEX cohort of preterm newborns: Correlation with the neurodevelopmental outcome at 2 years.

Purpose: The purpose of this study was to identify in the EPIRMEX cohort the correlations between MRI brain metrics, including diffuse excessive high signal intensities (DEHSI) obtained with an automated quantitative method and neurodevelopmental outcomes at 2 years.

Materials And Methods: A total of 390 very preterm infants (gestational age at birth≤32 weeks) who underwent brain MRI at term equivalent age at 1.5T (n=338) or 3T (n=52) were prospectively included.

Automated neurosurgical stereotactic planning for intraoperative use: a comprehensive review of the literature and perspectives.

The creation of intracranial stereotactic trajectories, from entry point to target point, is still mostly done manually by the neurosurgeon. The development of automated stereotactic planning tools has been described in the literature. This systematic review aims to assess the effectiveness of stereotactic planning procedure automation and develop tools for patients undergoing neurosurgical stereotactic procedures.

Comprehensive Review of 3D Segmentation Software Tools for MRI Usable for Pelvic Surgery Planning.

Patient-specific 3D modeling is the first step towards image-guided surgery, the actual revolution in surgical care. Pediatric and adolescent patients with rare tumors and malformations should highly benefit from these latest technological innovations, allowing personalized tailored surgery. This study focused on the pelvic region, located at the crossroads of the urinary, digestive, and genital channels with important vascular and nervous structures.

An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI.

Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI).

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner.

The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities.

Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value.

Dual-rotation C-arm cone-beam computed tomography to increase low-contrast detection.

Purpose: This paper investigates the capabilities of a dual-rotation C-arm cone-beam computed tomography (CBCT) framework to improve non-contrast-enhanced low-contrast detection for full volume or volume-of-interest (VOI) brain imaging.

Method: The idea is to associate two C-arm short-scan rotational acquisitions (spins): one over the full detector field of view (FOV) at low dose, and one collimated to deliver a higher dose to the central densest parts of the head. The angular sampling performed by each spin is allowed to vary in terms of number of views and angular positions.

A challenging issue: Detection of white matter hyperintensities in neonatal brain MRI.

The progress of magnetic resonance imaging (MRI) allows for a precise exploration of the brain of premature infants at term equivalent age. The so-called DEHSI (diffuse excessive high signal intensity) of the white matter of premature brains remains a challenging issue in terms of definition, and thus of interpretation. We propose a semi-automatic detection and quantification method of white matter hyperintensities in MRI relying on morphological operators and max-tree representations, which constitutes a powerful tool to help radiologists to improve their interpretation.

A flexible patch based approach for combined denoising and contrast enhancement of digital X-ray images.

Denoising and contrast enhancement play key roles in optimizing the trade-off between image quality and X-ray dose. However, these tasks present multiple challenges raised by noise level, low visibility of fine anatomical structures, heterogeneous conditions due to different exposure parameters, and patient characteristics. This work proposes a new method to address these challenges.

The Negative Cone Mosaic: A New Manifestation of the Optical Stiles-Crawford Effect in Normal Eyes.

Purpose: The purpose of this study was to describe a previously unreported manifestation of the optical Stiles-Crawford effect (oSCE) in normal eyes.

Methods: In a cohort of 50 normal subjects, the directional reflectance of cones in the retinal periphery was explored by flood-illuminated adaptive optics (FIAO) and optical coherence tomography (OCT).

Results: In 32 eyes (64%), off-axis FIAO images of the retinal periphery (∼15-20° from the fovea) showed variably sized patches of hyporeflective dots (called here negative mosaic) coexisting with hyperreflective (positive) cones.

Functional and high-resolution retinal imaging monitoring photoreceptor damage in acute macular neuroretinopathy.

Background: To report functional and high-resolution retinal imaging abnormalities, including adaptive optics (AO) throughout the course of acute macular neuroretinopathy (AMNR).

Methods: Two female patients (four eyes) with a diagnosis of AMNR were observed at the Clinical Investigation Center, CHNO des Quinze-Vingts, Paris, France. The patients underwent detailed ophthalmic examination including best-corrected visual acuity, slit-lamp examination, kinetic and static perimetry, full-field and multifocal electroretinogram, infrared reflectance, autofluorescence imaging and spectral-domain optical coherence tomography (SD-OCT) and AO fundus imaging at presentation and during follow-up.

Venous Nicking Without Arteriovenous Contact: The Role of the Arteriolar Microenvironment in Arteriovenous Nickings.

Importance: Arteriovenous nickings (AVNs) in the retina are the cause of retinal vein occlusions and are also surrogates of cerebrovascular aging. The prevalent mechanistic model of AVNs stating that arteries crush veins remains somewhat unchallenged despite the lack of evidence other than fundus photographs. Here, we observed that venous nicking may be observed in the absence of physical contact with an arteriole.

Multi-organ localization with cascaded global-to-local regression and shape prior.

We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize the global-to-local cascade of regression random forest to multiple organs. A first regressor encodes the global relationships between organs, learning simultaneously all organs parameters.

A cortical-subcortical syntax pathway linking Broca's area and the striatum.

Combinatorial syntax has been shown to be underpinned by cortical key regions such as Broca's area and temporal cortices, and by subcortical structures such as the striatum. The cortical regions are connected via several cortico-to-cortical tracts impacting syntactic processing (e.g.

Segmentation of embryonic and fetal 3D ultrasound images based on pixel intensity distributions and shape priors.

This paper presents a novel variational segmentation framework combining shape priors and parametric intensity distribution modeling for extracting the fetal envelope on 3D obstetric ultrasound images. To overcome issues related to poor image quality and missing boundaries, we inject three types of information in the segmentation process: tissue-specific parametric modeling of pixel intensities, a shape prior for the fetal envelope and a shape model of the fetus' back. The shape prior is encoded with Legendre moments and used to constraint the evolution of a level-set function.

Multi-organ localization combining global-to-local regression and confidence maps.

We propose a method for fast, accurate and robust localization of several organs in medical images. We generalize global-to-local cascades of regression forests [1] to multiple organs. A first regressor encodes global relationships between organs.

A reproducible automated segmentation algorithm for corneal epithelium cell images from in vivo laser scanning confocal microscopy.

Purpose: To evaluate an automated process to find borders of corneal basal epithelial cells in pictures obtained from in vivo laser scanning confocal microscopy (Heidelberg Retina Tomograph III with Rostock corneal module).

Methods: On a sample of 20 normal corneal epithelial pictures, images were segmented through an automated four-step segmentation algorithm. Steps of the algorithm included noise reduction through a fast Fourier transform (FFT) band-pass filter, image binarization with a mean value threshold, watershed segmentation algorithm on distance map to separate fused cells and Voronoi diagram segmentation algorithm (which gives a final mask of cell borders).