Removing segmentation inconsistencies with semi-supervised non-adjacency constraint.

The advent of deep learning has pushed medical image analysis to new levels, rapidly replacing more traditional machine learning and computer vision pipelines. However segmenting and labelling anatomical regions remains challenging owing to appearance variations, imaging artifacts, the paucity and variability of annotated data, and the difficulty of fully exploiting domain constraints such as anatomical knowledge about inter-region relationships. We address the last point, improving the network's region-labeling consistency by introducing NonAdjLoss, an adjacency-graph based auxiliary training loss that penalizes outputs containing regions with anatomically-incorrect adjacency relationships. NonAdjLoss supports both fully-supervised training and a semi-supervised extension in which it is applied to unlabeled supplementary training data. The approach substantially reduces segmentation anomalies on the MICCAI-2012, IBSRv2 brain MRI datasets and the Anatomy3 whole body CT dataset, especially when semi-supervised training is included.

OntoVIP: an ontology for the annotation of object models used for medical image simulation.

This paper describes the creation of a comprehensive conceptualization of object models used in medical image simulation, suitable for major imaging modalities and simulators. The goal is to create an application ontology that can be used to annotate the models in a repository integrated in the Virtual Imaging Platform (VIP), to facilitate their sharing and reuse. Annotations make the anatomical, physiological and pathophysiological content of the object models explicit. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology, that facilitates the consistent integration of the various modules extracted from several existing ontologies, i.e. FMA, PATO, MPATH, RadLex and ChEBI. Emphasis is put on methodology for achieving this extraction and integration. The most salient aspects of the ontology are presented, especially the organization in model layers, as well as its use to browse and query the model repository.

A virtual imaging platform for multi-modality medical image simulation.

This paper presents the Virtual Imaging Platform (VIP), a platform accessible at http://vip.creatis.insa-lyon.fr to facilitate the sharing of object models and medical image simulators, and to provide access to distributed computing and storage resources. A complete overview is presented, describing the ontologies designed to share models in a common repository, the workflow template used to integrate simulators, and the tools and strategies used to exploit computing and storage resources. Simulation results obtained in four image modalities and with different models show that VIP is versatile and robust enough to support large simulations. The platform currently has 200 registered users who consumed 33 years of CPU time in 2011.

Optimization of mean-shift scale parameters on the EGEE grid.

This paper studies the optimization of Mean-Shift (MS) image filtering scale parameters. A parameter sweep experiment representing 164 days of CPU is performed on the EGEE grid. The mathematical foundations of Mean-Shift and the grid environment used for the deployment are described in details. The experiments and results are then discussed highlighting the efficiency of gradient ascent algorithm for MS parameters optimization and a number of grid observations related to data transfers, reliability, task scheduling, CPU time and usability.

Magnetic resonance imaging method based on magnetic susceptibility effects to estimate bubble size in alveolar products: application to bread dough during proving.

Magnetic resonance imaging has proven its potential application in bread dough and gas cell monitoring studies, and dynamic processes such as dough proving and baking can be monitored. However, undesirable magnetic susceptibility effects often affect quantification studies, especially at high fields. A new low-field method is presented based on local assessment of porosity in spin-echo imaging, local characterization of signal loss in gradient-echo imaging and prediction of relaxation times by simulation to estimate bubble radii in bread dough during proving. Maps of radii showed different regions of dough constituting networks which evolved during proving. Mean radius and bubble distribution were assessed during proving.

Signal decay due to susceptibility-induced intravoxel dephasing on multiple air-filled cylinders: MRI simulations and experiments.

OBJECTIVE: Characterization of magnetic susceptibility artefacts with assessment of the gradient-echo signal decay function of echo time, pixel size, and object geometry in the case of air-filled cylinders embedded in water. MATERIALS AND METHODS: Experiments were performed with a 0.2 T magnet on a network of small interacting air-filled cylinders along with Magnetic resonance imaging (MRI) simulations integrating intravoxel dephasing. Signal decay over echo time was assessed at different pixel sizes on real and simulated images. The effects of radius, distance between cylinders and main magnetic field were studied using simulation. RESULTS: Signal loss was greater as echo time or pixel size increased. Voxel signal decay was not exponential but was weighted by sinus cardinalis functions integrating echo time, pixel size and field inhomogeneities which depended on main magnetic field strength and geometric configuration of the object. Simulation was able to model signal decay, even for a complex object constituted of several cylinders. The specific experimental signal modulation we observed was thus reproduced and explained by simulation. CONCLUSION: The quantitative signal decay approach at 0.2 T can be used in characterization studies in the case of locally regular air/water interfaces as the signal depends on object size relative to pixel size and is relevant to the geometric configuration. Moreover, the good concordance between simulation and experiments should lead to further studies of magnetic susceptibility effects with other objects such as networks of spheres. MRI simulation is thus a potential tool for molecular and porous media imaging.

Intensity non-uniformity correction in MRI: existing methods and their validation.

Magnetic resonance imaging is a popular and powerful non-invasive imaging technique. Automated analysis has become mandatory to efficiently cope with the large amount of data generated using this modality. However, several artifacts, such as intensity non-uniformity, can degrade the quality of acquired data. Intensity non-uniformity consists in anatomically irrelevant intensity variation throughout data. It can be induced by the choice of the radio-frequency coil, the acquisition pulse sequence and by the nature and geometry of the sample itself. Numerous methods have been proposed to correct this artifact. In this paper, we propose an overview of existing methods. We first sort them according to their location in the acquisition/processing pipeline. Sorting is then refined based on the assumptions those methods rely on. Next, we present the validation protocols used to evaluate these different correction schemes both from a qualitative and a quantitative point of view. Finally, availability and usability of the presented methods is discussed.