Segmentation fonctionnelle de séquences d'IRM rénales à rehaussement de contraste par quantification vectorielle

En Imagerie par Résonance Magnétique (IRM) à rehaussement de contraste, la segmentation des structures internes du rein est nécessaire pour une étude de la fonction rénale par compartiment. Pour éviter une segmentation manuelle fastidieuse, deux méthodes (semi-)automatiques, utilisant un algorithme de quantification vectorielle visant à regrouper les pixels rénaux d'après leurs vecteurs temps-intensité, sont proposées et validées sur des données réelles

Functional Semi-Automated Segmentation of Renal DCE-MRI Sequences

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.International audienceIn dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI), segmentation of internal kidney structures is essential for functional evaluation. Manual morphological segmentation of cortex, medulla and cavities remains difficult and time- consuming especially because the different renal compartments are hard to distinguish on a single image. We propose to test a semi-automated method to segment internal kidney structures from a DCE-MRI registered sequence. As the temporal intensity evolution is different in each of the three kidney compartments, pixels are sorted according to their time- intensity curves using a k-means partitioning algorithm. No ground truth is available to evaluate resulting segmentations so a manual segmentation by a radiologist is chosen as a reference. We first evaluate some similarity criteria between the functional segmentations and this reference. The same measures are then computed between another manual segmentation and the reference. Results are similar for the two types of comparisons

Functional Semi-Automated Segmentation of Renal DCE-MRI Sequences Using a Growing Neural Gas algorithm

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Objective Assessment Of Renal DCE-MRI Image Segmentation

ISSN 2076-1465International audienceIn dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of renal perfusion with injection of a contrast agent, the segmentation of kidney in regions of interest like cortex, medulla and pelvo-caliceal cavities is necessary for accurate functional evaluation. Several semiautomatic segmentation methods using time-intensity curves of renal voxels have been recently developed. Most of the time, quantitative result validation consists in comparisons with a manual segmentation by an expert. However it can be questionable to consider such a segmentation as a ground truth, especially because of intra- and inter-operator variability. Moreover it makes comparisons between results published by different authors delicate. We propose a method to built synthetic DCE-MRI sequences from typical time-intensity curves and an anatomical model that can be used for objective assessment of renal internal structures

Can gray seals maintain heading within areas of high tidal current? Preliminary results from numerical modeling and GPS observations

International audienceSeals are capable of navigation and orientation during long distance movements, even in absence of apparent landmarks, in open seas, and at night (e.g., Lowry et al. 1998, McConnell et al. 1999, Gjertz et al. 2000, Lesage et al. 2004). Several ideas have been put forwards about marine animals' ability to orientate and navigate at sea (Mills Flemming et al. 2006, Lohmann et al. 2008, Chapman et al. 2011). However, little work has been carried out on seals (but see Matsumura et al. 2011). A number of experiments have been conducted on captive seals in order to test their sensory systems and orientation capacities (e.g., Kowalewsky et al. 2006, Mauck et al. 2008), but such experiments are difficult to conduct on free-ranging seals. Modeling the animals' movements at sea in relation to environmental variables may elucidate the cues they use to orient and navigate. However, such free-ranging animal movements are always subject to the influence of local currents (Lohmann et al. 2008). Thus the incorporation of current data is necessary to reveal underlying navigational capabilities and strategies (Willis 2011). In this study, we model the observed sea surface tracks of two gray seals (Halichoerus grypus) that had crossed the English Channel in September 2011 (Fig. 1, 2). The seals (referred to as B23 and B24), were tracked by Fastloc GPS/GSM telemetry techniques. Their surface positions were drawn from the series of Fastloc GPS locations transmitted by GPS phone tags developed by the Sea Mammal Research Unit.2 These were glued to the animal's fur on the neck behind the head with quick-setting epoxy. The tags were configured to attempt Fastloc GPS locations every 10 min provided the seal was at the sea surface. Both seals were captured and tagged in the Mol ene archipelago, western Brittany, France

Functional semi-automated segmentation of renal DCE-MRI sequences using a Growing Neural Gas algorithm

International audienceIn dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI), segmentation of internal kidney structures like cortex, medulla and pelvo-caliceal cavities is necessary for functional assessment. Manual segmentation by a radiologist is fairly delicate because images are blurred and highly noisy. Moreover the different compartments cannot be delineated on a single image because they are not visible during the same perfusion phase for physiological reasons. Nevertheless the differences between temporal evolution of contrast in each anatomical region can be used to perform functional segmentation. We propose to test a semi-automated split and merge method based on time-intensity curves of renal pixels. Its first step requires a variant of the classical Growing Neural Gas algorithm. In the absence of ground truth for results assessment, a manual anatomical segmentation by a radiologist is considered as a reference. Some discrepancy criteria are computed between this segmentation and the functional one. As a comparison, the same criteria are evaluated between the reference and another manual segmentation

Functional semi-automated segmentation of renal DCE-MRI sequences: preliminary results

Dynamic contrast-enhanced (DCE) MRI sequences are the most used for renal functional analysis. Numerous parameters can be computed from time-intensity curves that require a time-consuming segmentation. The aim of this study, which was part of a national multicentric grant approved by the ethics committee, was to evaluate a semi-automated segmentation of renal DCE-MRI using vector quantization