Acute ischemic stroke lesion segmentation in non-contrast CT images using 3D convolutional neural networks

In this paper, an automatic algorithm aimed at volumetric segmentation of acute ischemic stroke lesion in non-contrast computed tomography brain 3D images is proposed. Our deep-learning approach is based on the popular 3D U-Net convolutional neural network architecture, which was modified by adding the squeeze-and-excitation blocks and residual connections. Robust pre-processing methods were implemented to improve the segmentation accuracy. Moreover, a specific patches sampling strategy was used to address the large size of medical images, to smooth out the effect of the class imbalance problem and to stabilize neural network training. All experiments were performed using five-fold cross-validation on the dataset containing non-contrast computed tomography volumetric brain scans of 81 patients diagnosed with acute ischemic stroke. Two radiology experts manually segmented images independently and then verified the labeling results for inconsistencies. The quantitative results of the proposed algorithm and obtained segmentation were measured by the Dice similarity coefficient, sensitivity, specificity and precision metrics. Our proposed model achieves an average Dice of $0.628\pm0.033$, sensitivity of $0.699\pm0.039$, specificity of $0.9965\pm0.0016$ and precision of $0.619\pm0.036$, showing promising segmentation results.Comment: 18 pages, 4 figures, 2 table

Catalytic peroxide fractionation processes for the green biorefinery of wood

SSCI-VIDE+CDFA+LDJ:CPIInternational audienceSoftwoo

Kinetic Study and Optimization of Catalytic Peroxide Delignification of Aspen Wood

International audienc

Green biorefinery of larch wood biomass to obtain the bioactive compounds, functional polymers and nanoporous materials

SSCI-VIDE+CDFA+LDJ:CPIInternational audience--

Isolation, Study and Application of Organosolv Lignins (Review)

Проведен анализ последних литературных источников, посвященных методам выделения растворимых органосольвентных лигнинов, их изучению физико-химическими методами и способам переработки в пористые аэрогели и жидкие углеводороды. Выполненный обзор литературы позволил обосновать выбор наиболее актуальных направлений исследований. Для выделения из древесины растворимых лигнинов, не содержащих серу, использованы методы каталитической пероксидной делигнификации в мягких условиях (температура ≤ 100 °С, атмосферное давление) и методы экстракции сверхкритическими органическими растворителями. Молекулярная масса и молекулярно-массовое распределение образцов этаноллигнина, выделенных из древесины осины и пихты, исследованы с помощью метода гель-проникающей хроматографии. Средневесовая молекулярная масса этаноллигнина пихты равна 478 Да, а этаноллигнина осины – 750 Да. Таким образом, изученные образцы этаноллигнина имеют довольно низкую молекулярную массу, что должно облегчить их дальнейшую переработку в жидкие углеводороды и аэрогели. Для деполимеризации органосольвентных лигнинов в жидкие углеводороды перспективно использовать процессы их каталитической конверсии в сверхкритических низших спиртах. В процессах термической конверсии лигнинов спирты не только экстрагируют продукты термической деполимеризации лигнина, но и способны их алкилировать, предотвращая вторичные реакции образования высокомолекулярных веществ. Твердые кислотные катализаторы позволяют повысить конверсию лигнина и выход жидких углеводородов. Для получения на основе лигнина нового класса нанопористых материалов использованы методы синтеза органических аэрогелей из смесей лигнина с другими природными полимерами и сшивающими агентами типа формальдегида. Установлено, что на строение и свойства пористых материалов аэрогельного типа оказывает влияние не только компонентный состав реакционной смеси, но и способ сушки. Сушка в докритических условиях приводит к образованию ксерогелей, в сверхкритических условиях – аэрогелей, лиофильная сушка – криогелей. Полученные пористые материалы могут иметь очень низкую плотность (около 0,2 г/см3), высокую удельную поверхность (около 500 м2/г) и объем пор около 5 см3/гThe analysis of the literature on the methods of soluble organosolv lignins isolation, their physicalchemical study and on the method of their processing to porous aerogels and liquid hydrocarbons was carried out. A review of the literature allowed us to choice of the most important areas of research. For isolation from wood the soluble lignins free from sulfur the methods of catalytic peroxide delignification at mild conditions (temperature ≤ 100 °C, atmospheric pressure) and methods of lignin extraction by supercritical organic solvents were used. Molecular mass and molecular-mass distribution of ethanol-lignin samples isolated from aspenwood and abies-wood were studied by gel-permeation chromatography. Weighted molecular mass of ethanol-lignin from abies wood is 478 Da and that from aspen wood ethanol-lignin – 750 Da. Thus, the studied samples of ethanol-lignin have rather low molecular mass, what should facilitate their further processing to liquid hydrocarbons and aerogels. For the depolymerization of organosolv lignins to liquid hydrocarbons the processes of their catalytic conversion in supercritical alcohols have good prospects for the use. In the processes of lignin thermal conversion alcohols can to extract the products of lignin depolymerization and to alkylate these products, preventing their repolymerization to high molecular mass substances. To obtain a new class of nanoporous materials based on lignin the methods of organic aerogels synthesis from mixtures of lignin with other natural polymers and crosslinking agents were applied. It was found that the structure and properties of porous materials of aerogel type depend not only from the reaction mixture composition but from the method of drying. Drying in subcritical conditions leads to the formation of xerogels, in supercritical conditions – to the formation aerogels and the freezdrying – of cryogels. Obtained porous materials can have very low density (around 0.2 g/cm3), high specific surface area (to 500 m2/g) and the pore volume near 5 cm3/

Central Yamal vegetation monitoring based on Sentinel-2 and Sentinel-1 imagery

In this study fusion of optical (Sentinel-2) and radar (Sentinel-1) imagery is presented for vegetation cover classification in polar Arctic environment of the Western Siberia. Sentinel-1 and Sentinel-2 images were analyzed using parametric rule classification. Results showed significantly improved land cover classification results based on contextual analysis. Synergy of Sentinel-2 bands 4 and 3 and Sentinel-1 dual polarization VV and VH images increased the classification accuracy significantly. Specifically, classification accuracy increased for two classes — Erect dwarf-shrub tundra with 6% and Fresh Water with 10%. The classification accuracy as well test sites were analyzed using in situ data collected during three fieldwork campaigns in August-September (2016–2018) in the surrounding of Bovanenkovo settlement

The use of Specim IQ, a hyperspectral camera, for plant analysis

Remote sensing using hyperspectral cameras is an important technology for non-destructive monitoring of plant pigment composition, which is closely related to their physiological state or infection with pathogens. The paper presents the experience of using Specim IQ, a mobile hyperspectral camera, to study common root rot (the pathogen is the fungus Bipolaris sorokiniana Shoem.) affecting the seedlings of four wheat varieties and to analyze the pulp of potato tubers of 82 lines and varieties. Spectral characteristics were obtained for seedlings and the most informative spectral features (indices) for root rot detection were determined based on the data obtained. Seedlings of control variants in the visible part of the spectrum show an increase in reflectance with a small peak in the green area (about 550 nm), then a decrease due to light absorption by plant pigments with an extremum at a wavelength of about 680 nm. Analysis of histograms of vegetation index values demonstrated that the TVI and MCARI indices are the most informative for detecting the pathogen on wheat seedlings according to hyperspectral survey data. For potato samples, regions of the spectrum were found that correspond to local maxima and minima of reflection. It was shown that the spectra of potato varieties have the greatest differences within wavelength ranges of 900-1000 nm and 400-450 nm, which in the former case may be associated with the level of water content, and in the latter, with the formation of melanin in the tubers. It was shown that according to the characteristics of the spectrum, the samples studied are divided into three groups, each characterized by increased or reduced intensity levels for the specified parts of the spectrum. In addition, minima in the reflection spectra corresponding to chlorophyll a were found for a number of varieties. The results demonstrate the capabilities of the Specim IQ camera for conducting hyperspectral analyses of plant objects