scholarly journals Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

2021 ◽  
Vol 15 ◽  
Author(s):  
Liying Peng ◽  
Lanfen Lin ◽  
Yusen Lin ◽  
Yen-wei Chen ◽  
Zhanhao Mo ◽  
...  
Keyword(s):  
Missing Data ◽  
Neural Development ◽  
Brain Size ◽  
Generative Adversarial Networks ◽  
First Year ◽  
Data Imputation ◽  
Mr Images ◽  
Adversarial Learning ◽  
Infant Brain ◽  
First Year Of Life

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Continuous missing data imputation with incomplete dataset by generative adversarial networks–based unsupervised learning for long-term bridge health monitoring

2021 ◽  
pp. 147592172110219
Author(s):  
Huachen Jiang ◽  
Chunfeng Wan ◽  
Kang Yang ◽  
Youliang Ding ◽  
Songtao Xue
Keyword(s):  
Missing Data ◽  
Health Monitoring ◽  
Signal Transmission ◽  
Imputation Accuracy ◽  
Generative Adversarial Networks ◽  
Data Imputation ◽  
Sensor Failure ◽  
Generative Adversarial Network ◽  
Missing Data Imputation ◽  
Adversarial Network

Wireless sensors are the key components of structural health monitoring systems. During the signal transmission, sensor failure is inevitable, among which, data loss is the most common type. Missing data problem poses a huge challenge to the consequent damage detection and condition assessment, and therefore, great importance should be attached. Conventional missing data imputation basically adopts the correlation-based method, especially for strain monitoring data. However, such methods often require delicate model selection, and the correlations for vehicle-induced strains are much harder to be captured compared with temperature-induced strains. In this article, a novel data-driven generative adversarial network (GAN) for imputing missing strain response is proposed. As opposed to traditional ways where correlations for inter-strains are explicitly modeled, the proposed method directly imputes the missing data considering the spatial–temporal relationships with other strain sensors based on the remaining observed data. Furthermore, the intact and complete dataset is not even necessary during the training process, which shows another great superiority over the model-based imputation method. The proposed method is implemented and verified on a real concrete bridge. In order to demonstrate the applicability and robustness of the GAN, imputation for single and multiple sensors is studied. Results show the proposed method provides an excellent performance of imputation accuracy and efficiency.

scholarly journals Learning-based deformable image registration for infant MR images in the first year of life

2017 ◽  
Vol 44 (1) ◽  
pp. 158-170 ◽  
Author(s):  
Shunbo Hu ◽  
Lifang Wei ◽  
Yaozong Gao ◽  
Yanrong Guo ◽  
Guorong Wu ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
First Year ◽  
Mr Images ◽  
First Year Of Life

scholarly journals Generative adversarial networks for imputing missing data for big data clinical research

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weinan Dong ◽  
Daniel Yee Tak Fong ◽  
Jin-sun Yoon ◽  
Eric Yuk Fai Wan ◽  
Laura Elizabeth Bedford ◽  
...  
Keyword(s):  
Big Data ◽  
Cohort Study ◽  
Missing Data ◽  
Clinical Research ◽  
Real World ◽  
Generative Adversarial Networks ◽  
Categorical Variables ◽  
Data Imputation ◽  
Link Type ◽  
Computation Speed

Abstract Background Missing data is a pervasive problem in clinical research. Generative adversarial imputation nets (GAIN), a novel machine learning data imputation approach, has the potential to substitute missing data accurately and efficiently but has not yet been evaluated in empirical big clinical datasets. Objectives This study aimed to evaluate the accuracy of GAIN in imputing missing values in large real-world clinical datasets with mixed-type variables. The computation efficiency of GAIN was also evaluated. The performance of GAIN was compared with other commonly used methods, MICE and missForest. Methods Two real world clinical datasets were used. The first was that of a cohort study on the long-term outcomes of patients with diabetes (50,000 complete cases), and the second was of a cohort study on the effectiveness of a risk assessment and management programme for patients with hypertension (10,000 complete cases). Missing data (missing at random) to independent variables were simulated at different missingness rates (20, 50%). The normalized root mean square error (NRMSE) between imputed values and real values for continuous variables and the proportion of falsely classified (PFC) for categorical variables were used to measure imputation accuracy. Computation time per imputation for each method was recorded. The differences in accuracy of different imputation methods were compared using ANOVA or non-parametric test. Results Both missForest and GAIN were more accurate than MICE. GAIN showed similar accuracy as missForest when the simulated missingness rate was 20%, but was more accurate when the simulated missingness rate was 50%. GAIN was the most accurate for the imputation of skewed continuous and imbalanced categorical variables at both missingness rates. GAIN had a much higher computation speed (32 min on PC) comparing to that of missForest (1300 min) when the sample size is 50,000. Conclusion GAIN showed better accuracy as an imputation method for missing data in large real-world clinical datasets compared to MICE and missForest, and was more resistant to high missingness rate (50%). The high computation speed is an added advantage of GAIN in big clinical data research. It holds potential as an accurate and efficient method for missing data imputation in future big data clinical research. Trial registration ClinicalTrials.gov ID: NCT03299010; Unique Protocol ID: HKUCTR-2232

The Development of Swallowing Respiratory Coordination

2009 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Maggie-Lee Huckabee
Keyword(s):  
Cortical Activation ◽  
First Year ◽  
Healthy Children ◽  
Nasal Airflow ◽  
Single Session ◽  
Primary Finding ◽  
Patterns Of Behavior ◽  
Cortical Modulation ◽  
First Year Of Life ◽  
Swallowing Apnea

Abstract Research exists that evaluates the mechanics of swallowing respiratory coordination in healthy children and adults as well and individuals with swallowing impairment. The research program summarized in this article represents a systematic examination of swallowing respiratory coordination across the lifespan as a means of behaviorally investigating mechanisms of cortical modulation. Using time-locked recordings of submental surface electromyography, nasal airflow, and thyroid acoustics, three conditions of swallowing were evaluated in 20 adults in a single session and 10 infants in 10 sessions across the first year of life. The three swallowing conditions were selected to represent a continuum of volitional through nonvolitional swallowing control on the basis of a decreasing level of cortical activation. Our primary finding is that, across the lifespan, brainstem control strongly dictates the duration of swallowing apnea and is heavily involved in organizing the integration of swallowing and respiration, even in very early infancy. However, there is evidence that cortical modulation increases across the first 12 months of life to approximate more adult-like patterns of behavior. This modulation influences primarily conditions of volitional swallowing; sleep and naïve swallows appear to not be easily adapted by cortical regulation. Thus, it is attention, not arousal that engages cortical mechanisms.

Gastric electrical activity in normal neonates during the first year of life. Effect of lactation with breast milk and formula

2001 ◽  
Vol 120 (5) ◽  
pp. A209-A209
Author(s):  
G RIEZZO ◽  
R CASTELLANA ◽  
T DEBELLIS ◽  
F LAFORGIA ◽  
F INDRIO ◽  
...  
Keyword(s):  
Breast Milk ◽  
Electrical Activity ◽  
First Year ◽  
Gastric Electrical Activity ◽  
First Year Of Life ◽  
Normal Neonates

Feeding and Function Pleasure in the First Year of Life

2013 ◽  
Author(s):  
Sylvia Brody ◽  
Sidney Axelrad
Keyword(s):  
First Year ◽  
And Function ◽  
First Year Of Life
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