Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively

Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in real-time segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.Comment: This work has been submitted to ISBI 201

Non-central chi estimation of multi-compartment models improves model selection by reducing overfitting

International audienceDiffusion images are known to be corrupted with a non-central chi (NCC)-distributed noise [1]. There has been a number of proposed image denoising methods that account for this particular noise distribution [1,2,3]. However, to the best of our knowledge, no study was performed to assess the influence of the noise model in the context of diffusion model estimation as was suggested in [4]. In particular, multi-compartment models [5] are an appealing class of models to describe the white matter microstructure but require the optimal number of compartments to be known a priori. Its estimation is no easy task since more complex models will always better fit the data, which is known as over-fitting. However, MCM estimation in the literature is performed assuming a Gaussian-distributed noise [5,6]. In this preliminary study, we aim at showing that using the appropriate NCC distribution for modelling the noise model reduces significantly the over-fitting, which could be helpful for unravelling model selection issues and obtaining better model parameter estimates

Randomized trial of polychromatic blue-enriched light for circadian phase shifting, melatonin suppression, and alerting responses.

Wavelength comparisons have indicated that circadian phase-shifting and enhancement of subjective and EEG-correlates of alertness have a higher sensitivity to short wavelength visible light. The aim of the current study was to test whether polychromatic light enriched in the blue portion of the spectrum (17,000 K) has increased efficacy for melatonin suppression, circadian phase-shifting, and alertness as compared to an equal photon density exposure to a standard white polychromatic light (4000 K). Twenty healthy participants were studied in a time-free environment for 7 days. The protocol included two baseline days followed by a 26-h constant routine (CR1) to assess initial circadian phase. Following CR1, participants were exposed to a full-field fluorescent light (1 × 10 14 photons/cm 2 /s, 4000 K or 17,000 K, n = 10/condition) for 6.5 h during the biological night. Following an 8 h recovery sleep, a second 30-h CR was performed. Melatonin suppression was assessed from the difference during the light exposure and the corresponding clock time 24 h earlier during CR1. Phase-shifts were calculated from the clock time difference in dim light melatonin onset time (DLMO) between CR1 and CR2. Blue-enriched light caused significantly greater suppression of melatonin than standard light ((mean ± SD) 70.9 ± 19.6% and 42.8 ± 29.1%, respectively, p \u3c 0.05). There was no significant difference in the magnitude of phase delay shifts. Blue-enriched light significantly improved subjective alertness (p \u3c 0.05) but no differences were found for objective alertness. These data contribute to the optimization of the short wavelength-enriched spectra and intensities needed for circadian, neuroendocrine and neurobehavioral regulation