101 research outputs found
Implementable deep learning for multi-sequence proton MRI lung segmentation: a multi-center, multi-vendor, and multi-disease study
Background
Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (1H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.
Purpose
Develop a generalizable CNN for lung segmentation in 1H-MRI, robust to pathology, acquisition protocol, vendor, and center.
Study type
Retrospective.
Population
A total of 809 1H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6â85); 42% females) and 31 healthy participants (median age (range): 34 (23â76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%); 50 participants (17%)) and external validation (164 scans (20%); 82 participants (28%)) sets.
Field Strength/Sequence
1.5-T and 3-T/3D spoiled-gradient recalled and ultrashort echo-time 1H-MRI.
Assessment
2D and 3D CNNs, trained on single-center, multi-sequence data, and the conventional spatial fuzzy c-means (SFCM) method were compared to manually delineated expert segmentations. Each method was validated on external data originating from several centers. Dice similarity coefficient (DSC), average boundary Hausdorff distance (Average HD), and relative error (XOR) metrics to assess segmentation performance.
Statistical Tests
KruskalâWallis tests assessed significances of differences between acquisitions in the testing set. Friedman tests with post hoc multiple comparisons assessed differences between the 2D CNN, 3D CNN, and SFCM. BlandâAltman analyses assessed agreement with manually derived lung volumes. A P value of <0.05 was considered statistically significant.
Results
The 3D CNN significantly outperformed its 2D analog and SFCM, yielding a median (range) DSC of 0.961 (0.880â0.987), Average HD of 1.63âmm (0.65â5.45) and XOR of 0.079 (0.025â0.240) on the testing set and a DSC of 0.973 (0.866â0.987), Average HD of 1.11âmm (0.47â8.13) and XOR of 0.054 (0.026â0.255) on external validation data.
Data Conclusion
The 3D CNN generated accurate 1H-MRI lung segmentations on a heterogenous dataset, demonstrating robustness to disease pathology, sequence, vendor, and center.
Evidence Level
4.
Technical Efficacy
Stage 1
Whole-genome sequencing reveals host factors underlying critical COVID-19
Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genesâincluding reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)âin critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease
Use of Volumetric Breast Density Measures for the Prediction of Weight and Body Mass Index
Preclinical development of noninvasive vascular occlusion with focused ultrasonic surgery for fetal therapy
OBJECTIVE: This study was undertaken to investigate the ability of focused ultrasonic surgery to occlude blood flow in vivo
AttentionâDeficit/Hyperactivity Disorder (ADHD): Interaction between socioeconomic status and parental history of ADHD
Gas hydrate formation probability distributions: The effect of shear and comparisons with Nucleation Theory
Gas hydrate formation is a stochastic phenomenon of considerable significance for any risk-based approach to flow assurance in the oil and gas industry. In principle, well-established results from nucleation theory offer the prospect of predictive models for hydrate formation probability in industrial production systems. In practice, however, heuristics are relied on when estimating formation risk for a given flowline subcooling or when quantifying kinetic hydrate inhibitor (KHI) performance. Here, we present statistically significant measurements of formation probability distributions for natural gas hydrate systems under shear, which are quantitatively compared with theoretical predictions. Distributions with over 100 points were generated using low-mass, Peltier-cooled pressure cells, cycled in temperature between 40 and â5 °C at up to 2 K·minâ1 and analyzed with robust algorithms that automatically identify hydrate formation and initial growth rates from dynamic pressure data. The application of shear had a significant influence on the measured distributions: at 700 rpm mass-transfer limitations were minimal, as demonstrated by the kinetic growth rates observed. The formation probability distributions measured at this shear rate had mean subcoolings consistent with theoretical predictions and steelâhydrateâwater contact angles of 14â26°. However, the experimental distributions were substantially wider than predicted, suggesting that phenomena acting on macroscopic length scales are responsible for much of the observed stochastic formation. Performance tests of a KHI provided new insights into how such chemicals can reduce the risk of hydrate blockage in flowlines. Our data demonstrate that the KHI not only reduces the probability of formation (by both shifting and sharpening the distribution) but also reduces hydrate growth rates by a factor of 2
Effect of Various Prelaying Levels of Dietary Calcium Upon Subsequent Performance in Chickens
Underestimation and overestimation of personal weight status: associations with socio-demographic characteristics and weight maintenance intentions
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