In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients—manually annotated by up to four raters—and to 65 comparable scans generated using tumor image simulation software Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%–85%), illustrating the difficulty of this task We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource

/pdf/the-multimodal-brain-tumor-image-segmentation-benchmark-2gfraj98dk.pdf

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

Brain tumor detection using fusion of hand crafted and deep learning features

A review on brain tumor segmentation of MRI images.

The means through which microbes and plants contribute to soil organic carbon (SOC) accumulation remain elusive due to challenges in disentangling the complex components of SOC. Here we use amino sugars and lignin phenols as tracers for microbial necromass and plant lignin components, respectively, and investigate their distribution in the surface soils across Mongolian grasslands in comparison with published data for other grassland soils of the world. While lignin phenols decrease, amino sugars increase with SOC contents in all examined grassland soils, providing continental-scale evidence for the key role of microbial necromass in SOC accumulation. Moreover, in contrast to clay’s control on amino sugar accumulation in fine-textured soils, aridity plays a central role in amino sugar accrual and lignin decomposition in the coarse-textured Mongolian soils. Hence, aridity shifts may have differential impacts on microbial-mediated SOC accumulation in grassland soils of varied textures. It remains unclear how microbes and plants contribute to soil organic carbon (SOC) accrual. Here, using biomarkers, the authors show that microbial necromass and plant-derived lignin components have divergent accumulation mechanisms and that microbial necromass plays a key role in SOC accumulation.

/pdf/divergent-accumulation-of-microbial-necromass-and-plant-51dvnvlh2v.pdf

Divergent accumulation of microbial necromass and plant lignin components in grassland soils.

Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested. Simple thresholds and statistical methods are unable to adequately segment the various elements of the GBM, such as local contrast enhancement, necrosis, and edema. Most voxel-based methods cannot achieve satisfactory results in larger data sets, and the methods based on generative or discriminative models have intrinsic limitations during application, such as small sample set learning and transfer. A new method was developed to overcome these challenges. Multimodal MR images are segmented into superpixels using algorithms to alleviate the sampling issue and to improve the sample representativeness. Next, features were extracted from the superpixels using multi-level Gabor wavelet filters. Based on the features, a support vector machine (SVM) model and an affinity metric model for tumors were trained to overcome the limitations of previous generative models. Based on the output of the SVM and spatial affinity models, conditional random fields theory was applied to segment the tumor in a maximum a posteriori fashion given the smoothness prior defined by our affinity model. Finally, labeling noise was removed using “structural knowledge” such as the symmetrical and continuous characteristics of the tumor in spatial domain. The system was evaluated with 20 GBM cases and the BraTS challenge data set. Dice coefficients were computed, and the results were highly consistent with those reported by Zikic et al. (MICCAI 2012, Lecture notes in computer science. vol 7512, pp 369–376, 2012). A brain tumor segmentation method using model-aware affinity demonstrates comparable performance with other state-of-the art algorithms.

Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features.

Quantifying volume and growth of a brain tumor is a primary prognostic measure and hence has received much attention in the medical imaging community. Most methods have sought a fully automatic segmentation, but the variability in shape and appearance of brain tumor has limited their success and further adoption in the clinic. In reaction, we present a semi-automatic brain tumor segmentation framework for multi-channel magnetic resonance (MR) images. This framework does not require prior model construction and only requires manual labels on one automatically selected slice. All other slices are labeled by an iterative multi-label Markov random field optimization with hard constraints. Structural trajectories—the medical image analog to optical flow—and 3D image over-segmentation are used to capture pixel correspondences between consecutive slices for pixel labeling. We show robustness and effectiveness through an evaluation on the 2012 MICCAI BRATS Challenge Dataset; our results indicate superior performance to baselines and demonstrate the utility of the constrained MRF formulation.

/pdf/semi-automatic-brain-tumor-segmentation-by-constrained-mrfs-3mgu8c9ett.pdf

Semi-automatic brain tumor segmentation by constrained MRFs using structural trajectories.

We model five classes of data in brain: brain white matter, brain gray matter, brain csf, tumor and edema. Denote the parameters of Gaussian component [1] φi = {φi, μμi, Σi}, where μμ!is mean of vector and Σ! is the covariance matrix. The φ! parameter is called the mixing coefficient and describes the relative weight of component i in the complete model. The complete model can be written ψ = {k,φ!,⋯ ,φ!}, where k is the number of component in the data. A mixture model on d-dimension data x is written:

/pdf/brain-tumor-segmentation-based-on-gmm-and-active-contour-vqs7jsr4b7.pdf

Brain tumor segmentation based on GMM and active contour method with a model-aware edge map

Abstract. We examined bulk soil properties and molecular biomarker distributions in surface soils from Inner Mongolian grasslands in order to understand the responses of soil organic matter to different land use. A total of 16 soils were collected from severely degraded grassland by overgrazing (DG), native grassland without apparent anthropogenic disturbance (NG), groundwater-sustaining grassland (GG) and restored grassland from previous potato cropland (RG). Compared to NG, soil organic carbon content was lower by 50% in DG, but higher by six-fold in GG and one-fold in RG. The δ13C values of soil organic carbon were –24.2 ± 0.6‰ in DG, –24.9 ± 0.6‰ in NG, –25.1 ± 0.1‰ in RG and –26.2 ± 0.6‰ in GG, reflecting different degradation degrees of soil organic matter or different water use efficiencies. The soils in DG contained the lowest abundance of aliphatic lipids (n-alkanes, n-alkanols, n-alkanoic acids, ω-hydroxylalkanoic acids and α-hydroxyalkanoic acids) and lignin-phenols, suggesting selective removal of these biochemically recalcitrant biomarkers with grassland degradation by microbial respiration or wind erosion. Compared to NG, the soils in GG and RG increased ω-hydroxylalkanoic acids by 60–70%, a biomarker for suberin from roots, and increased α-hydroxylalkanoic acids by 10–20%, a biomarker for both cutin and suberin. Our results demonstrate that the groundwater supply and cultivation–restoration practices in Inner Mongolian grasslands not only enhance soil organic carbon sequestration, but also change the proportions of shoot- versus root-derived carbon in soils. This finding has important implications for the global carbon cycle since root-derived aliphatic carbon has a longer residence time than the aboveground tissue-derived carbon in soils.

Soil organic matter dynamics under different land use in grasslands in Inner Mongolia (northern China)

Evidence has shown that some dietary polyphenols (flavonoids, phenolic acids, tannins, lignans, saponins, terpenoids, etc.) can alleviate alcoholic liver disease (ALD). Raspberries, the fourth most significant fruit worldwide, comprise a particular phytochemical profile encompassing ellagitannins and anthocyanins and exert many biological activities. In contrast to Chinese liquor (named Baijiu in China), the effect of two kinds of raspberry wines (fermentation and soaking types) on the liver is poorly understood. This study aimed to evaluate the effect of the two types of raspberry wines on the liver in a mouse model. The results showed that the livers of mice treated with raspberry wines produced less malondialdehyde, hepatic function markers, and proinflammatory cytokines, and less lipid accumulation in the serum and liver was also found compared to the mice treated with Baijiu. The activities of antioxidant markers in the liver from the raspberry wine supplementation group were higher than those receiving Baijiu. This study demonstrated that raspberry wines provoked more minor liver injury than Baijiu. The degree of liver damage caused by soaked raspberry wine was lower than that induced by fermented raspberry wine. 

Effect of soaked and fermented raspberry wines on the liver in mice

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