The stress‐induced photo‐luminescence response of tetrapodal shaped ZnO filler embedded in a silicone elastomer is used to demonstrate a novel concept for self‐reporting materials. Applied tensile ...stress can be followed in composites with low and high filler fractions by measuring the photoluminescence response of the T‐ZnO. The deformation of the interlocked ZnO network appears to be essential for the self‐reporting mechanism.
Purpose
To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X‐ray mammography can be categorized into malignant and benign with unenhanced magnetic ...resonance (MR) mammography with diffusion‐weighted imaging and T2‐weighted sequences.
Materials and Methods
From an asymptomatic screening cohort, 50 women with mammographically suspicious findings were examined with contrast‐enhanced breast MRI (ceMRI) at 1.5T. Out of this protocol an unenhanced, abbreviated diffusion‐weighted imaging protocol (ueMRI) including T2‐weighted, (T2w), diffusion‐weighted imaging (DWI), and DWI with background suppression (DWIBS) sequences and corresponding apparent diffusion coefficient (ADC) maps were extracted. From ueMRI‐derived radiomic features, three Lasso‐supervised machine‐learning classifiers were constructed and compared with the clinical performance of a highly experienced radiologist: 1) univariate mean ADC model, 2) unconstrained radiomic model, 3) constrained radiomic model with mandatory inclusion of mean ADC.
Results
The unconstrained and constrained radiomic classifiers consisted of 11 parameters each and achieved differentiation of malignant from benign lesions with a .632 + bootstrap receiver operating characteristics (ROC) area under the curve (AUC) of 84.2%/85.1%, compared to 77.4% for mean ADC and 95.9%/95.9% for the experienced radiologist using ceMRI/ueMRI.
Conclusion
In this pilot study we identified two ueMRI radiomics classifiers that performed well in the differentiation of malignant from benign lesions and achieved higher performance than the mean ADC parameter alone. Classification was lower than the almost perfect performance of a highly experienced breast radiologist. The potential of radiomics to provide a training‐independent diagnostic decision tool is indicated. A performance reaching the human expert would be highly desirable and based on our results is considered possible when the concept is extended in larger cohorts with further development and validation of the technique.
Level of Evidence: 1
Technical Efficacy: Stage 2
J. MAGN. RESON. IMAGING 2017;46:604–616
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large ...and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To address this challenge, deep learning models can be pre-trained on large image datasets without annotations using methods from the field of self-supervised learning. After pre-training, small annotated datasets are sufficient to fine-tune the models for a specific task. The most popular self-supervised pre-training approaches in medical imaging are based on contrastive learning. However, recent studies in natural image processing indicate a strong potential for masked autoencoder approaches. Our work compares state-of-the-art contrastive learning methods with the recently introduced masked autoencoder approach "SparK" for convolutional neural networks (CNNs) on medical images. Therefore, we pre-train on a large unannotated CT image dataset and fine-tune on several CT classification tasks. Due to the challenge of obtaining sufficient annotated training data in medical imaging, it is of particular interest to evaluate how the self-supervised pre-training methods perform when fine-tuning on small datasets. By experimenting with gradually reducing the training dataset size for fine-tuning, we find that the reduction has different effects depending on the type of pre-training chosen. The SparK pre-training method is more robust to the training dataset size than the contrastive methods. Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets.
BACKGROUND AND PURPOSE—ABC/2 is still widely accepted for volume estimations in spontaneous intracerebral hemorrhage (ICH) despite known limitations, which potentially accounts for controversial ...outcome-study results. The aim of this study was to establish and validate an automatic segmentation algorithm, allowing for quick and accurate quantification of ICH.
METHODS—A segmentation algorithm implementing first- and second-order statistics, texture, and threshold features was trained on manual segmentations with a random-forest methodology. Quantitative data of the algorithm, manual segmentations, and ABC/2 were evaluated for agreement in a study sample (n=28) and validated in an independent sample not used for algorithm training (n=30).
RESULTS—ABC/2 volumes were significantly larger compared with either manual or algorithm values, whereas no significant differences were found between the latter (P<0.0001; Friedman+Dunn’s multiple comparison). Algorithm agreement with the manual reference was strong (concordance correlation coefficient 0.95 lower 95% confidence interval 0.91) and superior to ABC/2 (concordance correlation coefficient 0.77 95% confidence interval 0.64). Validation confirmed agreement in an independent sample (algorithm concordance correlation coefficient 0.99 95% confidence interval 0.98, ABC/2 concordance correlation coefficient 0.82 95% confidence interval 0.72). The algorithm was closer to respective manual segmentations than ABC/2 in 52/58 cases (89.7%).
CONCLUSIONS—An automatic segmentation algorithm for volumetric analysis of spontaneous ICH was developed and validated in this study. Algorithm measurements showed strong agreement with manual segmentations, whereas ABC/2 exhibited its limitations, yielding inaccurate overestimations of ICH volume. The refined, yet time-efficient, quantification of ICH by the algorithm may facilitate evaluation of clot volume as an outcome predictor and trigger for surgical interventions in the clinical setting.
In 2014 the first synthesis of a transactinide carbonyl complex – seaborgium hexacarbonyl – was reported. This was achieved in gas-phase chemical experiments in a beam-free environment behind the ...recoil separator GARIS. Extending this work to heavier elements requires more efficient techniques to synthesize carbonyl complexes as production rates of transactinide elements drop with increasing atomic number. A novel approach was thus conceived, which retains the benefit of a beam-free environment but avoids the physical preseparation step. The latter reduces the yields for products of asymmetric reactions such as those used for the synthesis of suitable isotopes of Sg, Bh, Hs and Mt. For this a series of experiments with accelerator-produced radioisotopes of the lighter homologues W, Re and Os was carried out at the tandem accelerator of JAEA Tokai, Japan. A newly developed double-chamber system, which allows for a decoupled recoil ion thermalization and chemical complex formation, was used, which avoids the low-efficiency physical preseparation step. Here, we demonstrate the feasibility of this newly developed method using accelerator-produced short-lived radioisotopes of the 5d homologues of the early transactinides.
Human papillomavirus (HPV) infection is the leading cause of cervical cancer, and vaccination with HPV L1 capsid proteins has been successful in controlling it. However, vaccination coverage is not ...universal, particularly in developing countries, where 80% of all cervical cancer cases occur. Cost-effective vaccination could be achieved by expressing the L1 protein in plants. Various efforts have been made to produce the L1 protein in plants, including attempts to express it in chloroplasts for high-yield performance. However, manipulating chloroplast gene expression requires complex and difficult-to-control expression elements. In recent years, a family of nuclear-encoded, chloroplast-targeted RNA-binding proteins, the pentatricopeptide repeat (PPR) proteins, were described as key regulators of chloroplast gene expression. For example, PPR proteins are used by plants to stabilize and translate chloroplast mRNAs. The objective is to demonstrate that a PPR target site can be used to drive HPV L1 expression in chloroplasts. To test our hypothesis, we used biolistic chloroplast transformation to establish tobacco lines that express two variants of the HPV L1 protein under the control of the target site of the PPR protein CHLORORESPIRATORY REDUCTION2 (CRR2). The transgenes were inserted into a dicistronic operon driven by the plastid rRNA promoter. To determine the effectiveness of the PPR target site for the expression of the HPV L1 protein in the chloroplasts, we analyzed the accumulation of the transgenic mRNA and its processing, as well as the accumulation of the L1 protein in the transgenic lines. We established homoplastomic lines carrying either the HPV18 L1 protein or an HPV16B Enterotoxin::L1 fusion protein. The latter line showed severe growth retardation and pigment loss, suggesting that the fusion protein is toxic to the chloroplasts. Despite the presence of dicistronic mRNAs, we observed very little accumulation of monocistronic transgenic mRNA and no significant increase in CRR2-associated small RNAs. Although both lines expressed the L1 protein, quantification using an external standard suggested that the amounts were low. Our results suggest that PPR binding sites can be used to drive vaccine expression in plant chloroplasts; however, the factors that modulate the effectiveness of target gene expression remain unclear. The identification of dozens of PPR binding sites through small RNA sequencing expands the set of expression elements available for high-value protein production in chloroplasts.
A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious ...and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.
The form and magnitude of storm damage and stand disclosure patterns were assessed in 332 randomly chosen pure and regular stands of spruce (Picea abies L.) and beech (Fagus sylvatica L.) after storm ...LOTHAR, within a region of the Swiss Midlands. This data was analysed in relation to maximal wind speed, measured with Doppler radar techniques and other influential factors such as relief, allometric characteristics, silvicultural history, and neighbourhood. In addition, storm damage, assessed from aerial photographs over an extended perimeter (about 70,000 ha) was considered. A storm of the magnitude of LOTHAR (December 26 1999), with an average maximal wind speed of 45 m s⁻¹ (160 km h⁻¹) appears to have a highly chaotic wind field structure, with great spatial and temporal variation of wind gusts. Wind speeds were not a significant predictor for damage in spruce stands and only weakly influential for beech. The consequences of this high randomness were analysed to estimate the return time of such a storm at the stand level. It lies between 86 and 113 years for spruce, 357 and 408 for beech. Only a few independent variables were significant and the overall explanatory strength of the model was unexpectedly low (R ²=0.07 for spruce and 0.30 for beech). Among the more reliable predisposing factors were mixture and aspect combined with gradient. An admixture of 10% or more broadleaved tree species or wind-firm conifers like Douglas fir Pseudotsuga menziesii (Mirb.) Franco significantly reduced the vulnerability of spruce stands (by a factor of more than three). On wind-exposed aspects, damage was more than twice the average. Steeper slopes caused a significant reduction in susceptibility (by a factor of six for slopes over 50%, in comparison to gentle slopes <20%). Other factors such as height to diameter ratio of trees or time since last thinning did not appear to be significant predictors.
Coronary computed tomography angiography (CCTA) is increasingly the cornerstone in the management of patients with chronic coronary syndromes. This fact is reflected by current guidelines, which show ...a fundamental shift towards non-invasive imaging - especially CCTA. The guidelines for acute and stable coronary artery disease (CAD) of the European Society of Cardiology from 2019 and 2020 emphasize this shift. However, to fulfill this new role, a broader availability in adjunct with increased robustness of data acquisition and speed of data reporting of CCTA is needed. Artificial intelligence (AI) has made enormous progress for all imaging methodologies concerning (semi)-automatic tools for data acquisition and data post-processing, with outreach toward decision support systems. Besides onco- and neuroimaging, cardiac imaging is one of the main areas of application. Most current AI developments in the scenario of cardiac imaging are related to data postprocessing. However, AI applications (including radiomics) for CCTA also should enclose data acquisition (especially the fact of dose reduction) and data interpretation (presence and extent of CAD). The main effort will be to integrate these AI-driven processes into the clinical workflow, and to combine imaging data/results with further clinical data, thus - beyond the diagnosis of CAD- enabling prediction and forecast of morbidity and mortality. Furthermore, data fusing for therapy planning (e.g., invasive angiography/TAVI planning) will be warranted. The aim of this review is to present a holistic overview of AI applications in CCTA (including radiomics) under the umbrella of clinical workflows and clinical decision-making. The review first summarizes and analyzes applications for the main role of CCTA, i.e., to non-invasively rule out stable coronary artery disease. In the second step, AI applications for additional diagnostic purposes, i.e., to improve diagnostic power (CAC = coronary artery classifications), improve differential diagnosis (CT-FFR and CT perfusion), and finally improve prognosis (again CAC plus epi- and pericardial fat analysis) are reviewed.
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