Superpixels meet essential spectra for fast Raman hyperspectral microimaging.

In the context of spectral unmixing, essential information corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix which are indispensable to reproduce the full data matrix in a convex linear way. Essential information has recently been shown accessible on-the-fly via a decomposition of the measured spectra in the Fourier domain and has opened new perspectives for fast Raman hyperspectral microimaging. In addition, when some spatial prior is available about the sample, such as the existence of homogeneous objects in the image, further acceleration for the data acquisition procedure can be achieved by using superpixels. The expected gain in acquisition time is shown to be around three order of magnitude on simulated and real data with very limited distortions of the estimated spectrum of each object composing the images.

Machine learning at the energy and intensity frontiers of particle physics.

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

Assessment of Essential Information in the Fourier Domain to Accelerate Raman Hyperspectral Microimaging.

In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on the principal component analysis to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients, and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition.

Attention-Based Fusion of Ultrashort Voice Utterances and Depth Videos for Multimodal Person Identification.

Multimodal deep learning, in the context of biometrics, encounters significant challenges due to the dependence on long speech utterances and RGB images, which are often impractical in certain situations. This paper presents a novel solution addressing these issues by leveraging ultrashort voice utterances and depth videos of the lip for person identification. The proposed method utilizes an amalgamation of residual neural networks to encode depth videos and a Time Delay Neural Network architecture to encode voice signals. In an effort to fuse information from these different modalities, we integrate self-attention and engineer a noise-resistant model that effectively manages diverse types of noise. Through rigorous testing on a benchmark dataset, our approach exhibits superior performance over existing methods, resulting in an average improvement of 10%. This method is notably efficient for scenarios where extended utterances and RGB images are unfeasible or unattainable. Furthermore, its potential extends to various multimodal applications beyond just person identification.

Magnetic resonance elastography with guided pressure waves.

Magnetic resonance elastography aims to non-invasively and remotely characterize the mechanical properties of living tissues. To quantitatively and regionally map the shear viscoelastic moduli in vivo, the technique must achieve proper mechanical excitation throughout the targeted tissues. Although it is straightforward, ante manibus, in close organs such as the liver or the breast, which practitioners clinically palpate already, it is somewhat fortunately highly challenging to trick the natural protective barriers of remote organs such as the brain. So far, mechanical waves have been induced in the latter by shaking the surrounding cranial bones. Here, the skull was circumvented by guiding pressure waves inside the subject's buccal cavity so mechanical waves could propagate from within through the brainstem up to the brain. Repeatable, reproducible and robust displacement fields were recorded in phantoms and in vivo by magnetic resonance elastography with guided pressure waves such that quantitative mechanical outcomes were extracted in the human brain.

A Generic Formula and Some Special Cases for the Kullback-Leibler Divergence between Central Multivariate Cauchy Distributions.

This paper introduces a closed-form expression for the Kullback-Leibler divergence (KLD) between two central multivariate Cauchy distributions (MCDs) which have been recently used in different signal and image processing applications where non-Gaussian models are needed. In this overview, the MCDs are surveyed and some new results and properties are derived and discussed for the KLD. In addition, the KLD for MCDs is showed to be written as a function of Lauricella D-hypergeometric series FD(p). Finally, a comparison is made between the Monte Carlo sampling method to approximate the KLD and the numerical value of the closed-form expression of the latter. The approximation of the KLD by Monte Carlo sampling method are shown to converge to its theoretical value when the number of samples goes to the infinity.

Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.

The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of 5% correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light.

On the value of CTIS imagery for neural-network-based classification: a simulation perspective.

The computed tomography imaging spectrometer (CTIS) is a snapshot hyperspectral imaging system. Its output is a 2D image of multiplexed spatiospectral projections of the hyperspectral cube of the scene. Traditionally, the 3D cube is reconstructed from this image before further analysis. In this paper, we show that it is possible to learn information directly from the CTIS raw output, by training a neural network to perform binary classification on such images. The use case we study is an agricultural one, as snapshot imagery is used substantially in this field: the detection of apple scab lesions on leaves. To train the network appropriately and to study several degrees of scab infection, we simulated CTIS images of scabbed leaves. This was made possible with a novel CTIS simulator, where special care was taken to preserve realistic pixel intensities compared to true images. To the best of our knowledge, this is the first application of compressed learning on a simulated CTIS system.

Toward Joint Acquisition-Annotation of Images with Egocentric Devices for a Lower-Cost Machine Learning Application to Apple Detection.

Since most computer vision approaches are now driven by machine learning, the current bottleneck is the annotation of images. This time-consuming task is usually performed manually after the acquisition of images. In this article, we assess the value of various egocentric vision approaches in regard to performing joint acquisition and automatic image annotation rather than the conventional two-step process of acquisition followed by manual annotation. This approach is illustrated with apple detection in challenging field conditions. We demonstrate the possibility of high performance in automatic apple segmentation (Dice 0.85), apple counting (88 percent of probability of good detection, and 0.09 true-negative rate), and apple localization (a shift error of fewer than 3 pixels) with eye-tracking systems. This is obtained by simply applying the areas of interest captured by the egocentric devices to standard, non-supervised image segmentation. We especially stress the importance in terms of time of using such eye-tracking devices on head-mounted systems to jointly perform image acquisition and automatic annotation. A gain of time of over 10-fold by comparison with classical image acquisition followed by manual image annotation is demonstrated.

Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line.

Data sharing between technology companies and academic health researchers has multiple health care, scientific, social, and business benefits. Many companies remain wary about such sharing because of unaddressed concerns about ethics, data security, logistics, and public relations. Without guidance on these issues, few companies are willing to take on the potential work and risks involved in noncommercial data sharing, and the scientific and societal potential of their data goes unrealized. In this paper, we describe the 18-month long pilot of a data-sharing program led by Crisis Text Line (CTL), a not-for-profit technology company that provides a free 24/7 text line for people in crisis. The primary goal of the data-sharing pilot was to design, develop, and implement a rigorous framework of principles and protocols for the safe and ethical sharing of user data. CTL used a stakeholder-based policy process to develop a feasible and ethical data-sharing program. The process comprised forming a data ethics committee; identifying policy challenges and solutions; announcing the program and generating interest; and revising the policy and launching the program. Once the pilot was complete, CTL examined how well the program ran and compared it with other potential program models before putting in place the program that was most suitable for its organizational needs. By drawing on CTL's experiences, we have created a 3-step set of guidelines for other organizations that wish to develop their own data-sharing program with academic researchers. The guidelines explain how to (1) determine the value and suitability of the data and organization for creating a data-sharing program; (2) decide on an appropriate data sharing and collaboration model; and (3) develop protocols and technical solutions for safe and ethical data sharing and the best organizational structure for implementing the program. An internal evaluation determined that the pilot satisfied CTL's goals of sharing scientific data and protecting client confidentiality. The policy development process also yielded key principles and protocols regarding the ethical challenges involved in data sharing that can be applied by other organizations. Finally, CTL's internal review of the pilot program developed a number of alternative models for sharing data that will suit a range of organizations with different priorities and capabilities. In implementing and studying this pilot program, CTL aimed both to optimize its own future data-sharing programs and to inform similar decisions made by others. Open data programs are both important and feasible to establish. With careful planning and appropriate resources, data sharing between big data companies and academic researchers can advance their shared mission to benefit society and improve lives.

Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring-An Overview.

As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health.

Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke.

PURPOSE: The robustness of a recently introduced globally convergent deconvolution algorithm with temporal and edge-preserving spatial regularization for the deconvolution of dynamic susceptibility contrast perfusion magnetic resonance imaging is assessed in the context of ischemic stroke. THEORY AND METHODS: Ischemic tissues are not randomly distributed in the brain but form a spatially organized entity. The addition of a spatial regularization term allows to take into account this spatial organization contrarily to the sole temporal regularization approach which processes each voxel independently. The robustness of the spatial regularization in relation to shape variability, hemodynamic variability in tissues, noise in the magnetic resonance imaging apparatus, and uncertainty on the arterial input function selected for the deconvolution is addressed via an original in silico validation approach. RESULTS: The deconvolution algorithm proved robust to the different sources of variability, outperforming temporal Tikhonov regularization in most realistic conditions considered. The limiting factor is the proper estimation of the arterial input function. CONCLUSION: This study quantified the robustness of a spatio-temporal approach for dynamic susceptibility contrast-magnetic resonance imaging deconvolution via a new simulator. This simulator, now accessible online, is of wide applicability for the validation of any deconvolution algorithm. Magn Reson Med 78:1981-1990, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Discrepancy Between Cerebral Structure and Cognitive Functioning: A Review.

Neuroscientists typically assume that human mental functions are generated by the brain and that its structural elements, including the different cell layers and tissues that form the neocortex, play specific roles in this complex process. Different functional units are thought to complement one another to create an integrated self-awareness or episodic memory. Still, findings that pertain to brain dysplasia and brain lesions indicate that in some individuals there is a considerable discrepancy between the cerebral structures and cognitive functioning. This seems to question the seemingly well-defined role of these brain structures. This article provides a review of such remarkable cases. It contains overviews of noteworthy aspects of hydrocephalus, hemihydranencephaly, hemispherectomy, and certain abilities of "savants." We add considerations on memory processing, comment on the assumed role of neural plasticity in these contexts, and highlight the importance of taking such anomalies into account when formulating encompassing models of brain functioning.

Capture and coding of industry and occupation measures: Findings from eight National Program of Cancer Registries states.

BACKGROUND: Although data on industry and occupation (I&O) are important for understanding cancer risks, obtaining standardized data is challenging. This study describes the capture of specific I&O text and the ability of a web-based tool to translate text into standardized codes. METHODS: Data on 62 525 cancers cases received from eight National Program of Cancer Registries (NPCR) states were submitted to a web-based coding tool developed by the National Institute for Occupational Safety and Health for translation into standardized I&O codes. We determined the percentage of sufficiently analyzable codes generated by the tool. RESULTS: Using the web-based coding tool on data obtained from chart abstraction, the NPCR cancer registries achieved between 48% and 75% autocoding, but only 12-57% sufficiently analyzable codes. CONCLUSIONS: The ability to explore associations between work-related exposures and cancer is limited by current capture and coding of I&O data. Increased training of providers and registrars, as well as software enhancements, will improve the utility of I&O data.

Validity of shape as a predictive biomarker of final infarct volume in acute ischemic stroke.

BACKGROUND AND PURPOSE: This study examines whether lesion shape documented on magnetic resonance diffusion-weighted imaging during acute stroke improves the prediction of the final infarct volume compared with lesion volume only. METHODS: Diffusion-weighted imaging data and clinical information were retrospectively reviewed in 110 consecutive patients who underwent (n=67) or not (n=43) thrombolytic therapy for acute ischemic stroke. Three-dimensional shape analysis was performed on admission diffusion-weighted imaging data and 5 shape descriptors were developed. Final infarct volume was measured on T2-fluid-attenuated inversion recovery imaging data performed 30 days after stroke. RESULTS: Shape analysis of acute ischemic lesion and more specifically the ratio of the bounding box volume to the lesion volume before thrombolytic treatment improved the prediction of the final infarct for patients undergoing thrombolysis (R(2)=0.86 in model with volume; R(2)=0.98 in model with volume and shape). CONCLUSIONS: Our findings suggest that lesion shape contains important predictive information and reflects important environmental factors that might determine the progression of ischemia from the core.

Cerebral collateral flow defines topography and evolution of molecular penumbra in experimental ischemic stroke.

Intracranial collaterals are dynamically recruited after arterial occlusion and are emerging as a strong determinant of tissue outcome in both human and experimental ischemic stroke. The relationship between collateral flow and ischemic penumbra remains largely unexplored in pre-clinical studies. The aim of the present study was to investigate the pattern of collateral flow with regard to penumbral tissue after transient middle cerebral artery (MCA) occlusion in rats. MCA was transiently occluded (90min) by intraluminal filament in adult male Wistar rats (n=25). Intracranial collateral flow was studied in terms of perfusion deficit and biosignal fluctuation analyses using multi-site laser Doppler monitoring. Molecular penumbra was defined by topographical mapping and quantitative signal analysis of Heat Shock Protein 70kDa (HSP70) immunohistochemistry. Functional deficit and infarct volume were assessed 24h after ischemia induction. The results show that functional performance of intracranial collaterals during MCA occlusion inversely correlated with HSP70 immunoreactive areas in both the cortex and the striatum, as well as with infarct size and functional deficit. Intracranial collateral flow was associated with reduced areas of both molecular penumbra and ischemic core and increased areas of intact tissue in rats subjected to MCA occlusion followed by reperfusion. Our findings prompt the development of collateral therapeutics to provide tissue-saving strategies in the hyper-acute phase of ischemic stroke prior to recanalization therapy.