Improving Automatic Renal Segmentation in Clinically Normal and Abnormal Paediatric DCE-MRI via Contrast Maximisation and Convolutional Networks for Computing Markers of Kidney Function.

There is a growing demand for fast, accurate computation of clinical markers to improve renal function and anatomy assessment with a single study. However, conventional techniques have limitations leading to overestimations of kidney function or failure to provide sufficient spatial resolution to target the disease location. In contrast, the computer-aided analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) could generate significant markers, including the glomerular filtration rate (GFR) and time-intensity curves of the cortex and medulla for determining obstruction in the urinary tract. This paper presents a dual-stage fully modular framework for automatic renal compartment segmentation in 4D DCE-MRI volumes. (1) Memory-efficient 3D deep learning is integrated to localise each kidney by harnessing residual convolutional neural networks for improved convergence; segmentation is performed by efficiently learning spatial-temporal information coupled with boundary-preserving fully convolutional dense nets. (2) Renal contextual information is enhanced via non-linear transformation to segment the cortex and medulla. The proposed framework is evaluated on a paediatric dataset containing 60 4D DCE-MRI volumes exhibiting varying conditions affecting kidney function. Our technique outperforms a state-of-the-art approach based on a GrabCut and support vector machine classifier in mean dice similarity (DSC) by 3.8% and demonstrates higher statistical stability with lower standard deviation by 12.4% and 15.7% for cortex and medulla segmentation, respectively.

A low-glycemic-load versus low-fat diet in the treatment of fatty liver in obese children.

BACKGROUND: Fatty liver is highly prevalent among obese children and represents a major risk factor for chronic liver diseases and severe metabolic complications. METHODS: We randomly assigned 17 obese children 8-17 years of age with fatty liver to either an experimental low-glycemic-load or conventional low-fat diet for 6 months. Participants in both groups received nutrition education and behavioral counseling of equal intensity. The primary outcome was hepatic lipid content measured by proton magnetic resonance spectroscopy. Secondary outcomes included change in visceral fat, BMI, anthropometrics, alanine aminotransferase (ALT), and insulin resistance. RESULTS: A total of 16 participants completed the study. Reported glycemic load decreased in the low-glycemic-load group and reported dietary fat decreased in the low-fat group. At baseline, liver fat was 23.8% [standard deviation (SD) 12.2] in the low-glycemic-load group and 29.3% (14.1) in the low-fat group. Liver fat decreased substantially in both groups at 6 months expressed as absolute percentage change, with no between-group differences [-8.8 (standard error (SE) 4.1) vs. -10.5 (3.7)%, respectively, p=0.76 for group×time interaction]. Secondary outcomes also improved on both diets, with no between-group differences. Baseline and change in ALT were strongly associated with hepatic fat content. CONCLUSIONS: Weight-reducing diets focused either on glycemic load or dietary fat improved hepatic steatosis over 6 months. Additional research is needed to determine whether these diets differ in effectiveness over the long term. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00480922.