248 research outputs found
Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging.
Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%)
Narrow genetic base in forest restoration with holm oak (Quercus ilex L.) in Sicily
In order to empirically assess the effect of actual seed sampling strategy on
genetic diversity of holm oak (Quercus ilex) forestations in Sicily, we have
analysed the genetic composition of two seedling lots (nursery stock and
plantation) and their known natural seed origin stand by means of six nuclear
microsatellite loci. Significant reduction in genetic diversity and significant
difference in genetic composition of the seedling lots compared to the seed
origin stand were detected. The female and the total effective number of
parents were quantified by means of maternity assignment of seedlings and
temporal changes in allele frequencies. Extremely low effective maternity
numbers were estimated (Nfe 2-4) and estimates accounting for both
seed and pollen donors gave also low values (Ne 35-50). These values
can be explained by an inappropriate forestry seed harvest strategy limited to
a small number of spatially close trees
GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis
Unsupervised learning can discover various unseen diseases, relying on
large-scale unannotated medical images of healthy subjects. Towards this,
unsupervised methods reconstruct a single medical image to detect outliers
either in the learned feature space or from high reconstruction loss. However,
without considering continuity between multiple adjacent slices, they cannot
directly discriminate diseases composed of the accumulation of subtle
anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study has
shown how unsupervised anomaly detection is associated with disease stages.
Therefore, we propose a two-step method using Generative Adversarial
Network-based multiple adjacent brain MRI slice reconstruction to detect AD at
various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1
loss---trained on 3 healthy slices to reconstruct the next 3
ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss
(e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground
truth images. The results show that we can reliably detect AD at a very early
stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late
stage much more accurately with AUC 0.917; since our method is fully
unsupervised, it should also discover and alert any anomalies including rare
disease.Comment: 10 pages, 4 figures, Accepted to Lecture Notes in Bioinformatics
(LNBI) as a volume in the Springer serie
IntersectGAN: Learning Domain Intersection for Generating Images with Multiple Attributes
Generative adversarial networks (GANs) have demonstrated great success in
generating various visual content. However, images generated by existing GANs
are often of attributes (e.g., smiling expression) learned from one image
domain. As a result, generating images of multiple attributes requires many
real samples possessing multiple attributes which are very resource expensive
to be collected. In this paper, we propose a novel GAN, namely IntersectGAN, to
learn multiple attributes from different image domains through an intersecting
architecture. For example, given two image domains and with certain
attributes, the intersection denotes a new domain where images
possess the attributes from both and domains. The proposed
IntersectGAN consists of two discriminators and to distinguish
between generated and real samples of different domains, and three generators
where the intersection generator is trained against both discriminators. And an
overall adversarial loss function is defined over three generators. As a
result, our proposed IntersectGAN can be trained on multiple domains of which
each presents one specific attribute, and eventually eliminates the need of
real sample images simultaneously possessing multiple attributes. By using the
CelebFaces Attributes dataset, our proposed IntersectGAN is able to produce
high quality face images possessing multiple attributes (e.g., a face with
black hair and a smiling expression). Both qualitative and quantitative
evaluations are conducted to compare our proposed IntersectGAN with other
baseline methods. Besides, several different applications of IntersectGAN have
been explored with promising results
Beyond solid-state lighting: Miniaturization, hybrid integration, and applications og GaN nano- and micro-LEDs
Gallium Nitride (GaN) light-emitting-diode (LED) technology has been the revolution in modern lighting. In the last decade, a huge global market of efficient, long-lasting and ubiquitous white light sources has developed around the inception of the Nobel-price-winning blue GaN LEDs. Today GaN optoelectronics is developing beyond lighting, leading to new and innovative devices, e.g. for micro-displays, being the core technology for future augmented reality and visualization, as well as point light sources for optical excitation in communications, imaging, and sensing. This explosion of applications is driven by two main directions: the ability to produce very small GaN LEDs (microLEDs and nanoLEDs) with high efficiency and across large areas, in combination with the possibility to merge optoelectronic-grade GaN microLEDs with silicon microelectronics in a fully hybrid approach. GaN LED technology today is even spreading into the realm of display technology, which has been occupied by organic LED (OLED) and liquid crystal display (LCD) for decades. In this review, the technological transition towards GaN micro- and nanodevices beyond lighting is discussed including an up-to-date overview on the state of the art
Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging
Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute-and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e. g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%)
Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting
Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making
Communicable Diseases Prioritized for Surveillance and Epidemiological Research: Results of a Standardized Prioritization Procedure in Germany, 2011
To establish strategic priorities for the German national public health institute (RKI) and guide the institute's mid-term strategic decisions, we prioritized infectious pathogens in accordance with their importance for national surveillance and epidemiological research.We used the Delphi process with internal (RKI) and external experts and a metric-consensus approach to score pathogens according to ten three-tiered criteria. Additional experts were invited to weight each criterion, leading to the calculation of a median weight by which each score was multiplied. We ranked the pathogens according to the total weighted score and divided them into four priority groups.., Respiratory syncytial virus or Hantavirus) indicate a possible under-recognised importance within the current German public health framework. A process to strengthen respective surveillance systems and research has been started. The prioritization methodology has worked well; its modular structure makes it potentially useful for other settings
ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI
Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe
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