Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems

Individual Responses to an 8-Week Neuromuscular Training Intervention in Trained Pre-Pubescent Female Artistic Gymnasts

This study examined individual responses in leg stiffness, reactive strength index (RSI), movement proficiency (deep overhead squat and in-line lunge), and trunk muscular endurance (flexor and extensor tests) in young female gymnasts following an 8-week neuromuscular training intervention. Thirty-four pre-peak height velocity (PHV) female gymnasts were divided into either an experimental group (EXP n = 17) or control group (CON n = 17). The EXP replaced their normal gymnastics physical preparation with a neuromuscular training program, while the CON continued with their habitual gymnastics program. Chi square analysis showed that the EXP resulted in significantly more positive responders compared to CON for measures of leg stiffness (41% versus 12% responded positively), extensor muscular endurance, (76% versus 29%), and competency in the deep overhead squat, (76% versus 29%) and in-line lunge (left lead leg) (65% versus 18%). Conversely, the number of positive responders for RSI (53% versus 61%), the flexor endurance test (88% versus 53%), and the right in-line lunge (47% versus 35%) were not significantly different between groups. These findings suggest that most young gymnasts responded positively to neuromuscular training from the perspective of improving movement proficiency and trunk endurance; however, changes in leg stiffness and RSI were more variable and may require higher intensities to realise further adaptations

Automatic segmentation of MR brain images with a convolutional neural network

Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network. To ensure that the method obtains accurate segmentation details as well as spatial consistency, the network uses multiple patch sizes and multiple convolution kernel sizes to acquire multi-scale information about each voxel. The method is not dependent on explicit features, but learns to recognise the information that is important for the classification based on training data. The method requires a single anatomical MR image only. The segmentation method is applied to five different data sets: coronal T2-weighted images of preterm infants acquired at 30 weeks postmenstrual age (PMA) and 40 weeks PMA, axial T2- weighted images of preterm infants acquired at 40 weeks PMA, axial T1-weighted images of ageing adults acquired at an average age of 70 years, and T1-weighted images of young adults acquired at an average age of 23 years. The method obtained the following average Dice coefficients over all segmented tissue classes for each data set, respectively: 0.87, 0.82, 0.84, 0.86 and 0.91. The results demonstrate that the method obtains accurate segmentations in all five sets, and hence demonstrates its robustness to differences in age and acquisition protocol

Technical design

To convert Bergenmeersen from a flood control area (FCA) to a flood control area with controlled reduced tide (FCA-CRT), the existing dykes were modified and a new inlet and outlet construction was built. This chapter outlines the hydraulic and geotechnical design. This encompasses raising the existing ring dyke around the area, the new stability calculations and the modified dyke revetment along the water and land side. The inlet and outlet structure is also described. The hydraulic boundary conditions are extremely important to the design

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks

The Physiological Demands of Youth Artistic Gymnastics; Applications to Strength and Conditioning

The sport of artistic gymnastics involves a series of complex events that can expose young gymnasts to relatively high forces. The sport is recognized as attracting early specialization, in which young children are exposed to a high volume of sports-specific training. Leading world authorities advocate that young athletes should participate in strength and conditioning related activities in order to increase athlete robustness and reduce the relative risk of injury. The purpose of this commentary is to provide a needs analysis of artistic gymnastics, and to highlight key issues surrounding training that practitioners should consider when working with this unique population

Visual category representations in the infant brain

Visual categorization is a human core cognitive capacity1,2 that depends on the development of visual category representations in the infant brain.3,4,5,6,7 However, the exact nature of infant visual category representations and their relationship to the corresponding adult form remains unknown.8 Our results clarify the nature of visual category representations from electroencephalography (EEG) data in 6- to 8-month-old infants and their developmental trajectory toward adult maturity in the key characteristics of temporal dynamics,2,9 representational format,10,11,12 and spectral properties.13,14 Temporal dynamics change from slowly emerging, developing representations in infants to quickly emerging, complex representations in adults. Despite those differences, infants and adults already partly share visual category representations. The format of infants' representations is visual features of low to intermediate complexity, whereas adults' representations also encode high-complexity features. Theta band activity contributes to visual category representations in infants, and these representations are shifted to the alpha/beta band in adults. Together, we reveal the developmental neural basis of visual categorization in humans, show how information transmission channels change in development, and demonstrate the power of advanced multivariate analysis techniques in infant EEG research for theory building in developmental cognitive science

DIODE-LASER BASED PHOTO-ACOUSTIC SPECTROSCOPY IN ATMOSPHERIC NO­2 DETECTION

We have developed a simple, low cost, and compact NO2 detection system. It’s based on photoacoustic spectroscopy (PAS) method uses a diode laser as a source of radiation. The PAS system has a detection limit of 10 ppbv for NO2. With this set-up we were able to detect the NO2 concentration from urban air near our campus. We have also investigated the NO2 dissociation effect on the PAS system via NO measurements using a direct absorption spectroscopy method on quantum cascade laser (QCL) system. Keywords: photoacoustic spectroscop

The influence of biological maturity on dynamic force–time variables and vaulting performance in young female gymnasts

Purpose: This cross-sectional study investigated dynamic force–time variables and vaulting performance in young female gymnasts of different maturity status. Methods: 120 gymnasts aged 5–14 years were sub-divided into maturity groupings using percent of predicted adult height (%PAH) attained. Participants performed three jumping protocols, the squat jump (SJ), countermovement jump (CMJ) and drop jump (DJ), before completing straight jump vaults that were recorded using two-dimensional video. Results: Jumping performance improved with biological maturity evidenced by the most mature gymnasts’ producing significantly more absolute force (P \u3c 0.05; all d \u3e 0.78), impulse (P \u3c 0.05; all d \u3e 0.75) and power (P \u3c 0.05; all d \u3e 0.91) than the least mature group, resulting in the greater jump heights (P \u3c 0.05; all d \u3e 0.70). While, no significant differences were observed in relative peak force across multiple tests, measures of relative peak power did significantly increase with maturity. Based upon regression analyses, maturation was found to influence vertical take-off velocity during vaulting, explaining 41% of the variance in each jumping protocol. Across all tests, the DJ was found to have the highest predictive ability of vaulting vertical take-off velocity, explaining 55% of the total variance. Conclusion: Biological maturation impacts jump height and underpinning mechanical variables in young female gymnasts. Vaulting vertical take-off velocity appears to be influenced by maturation and various dynamic force–time variables, particularly those during DJ, which had the highest explained total variance