Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net

Segmentation of Striatal Brain Structures from High Resolution PET Images

We propose and evaluate an automatic segmentation method for extracting striatal brain structures (caudate, putamen, and ventral striatum) from parametric 11C-raclopride positron emission tomography (PET) brain images. We focus on the images acquired using a novel brain dedicated high-resolution (HRRT) PET scanner. The segmentation method first extracts the striatum using a deformable surface model and then divides the striatum into its substructures based on a graph partitioning algorithm. The weighted kernel k-means algorithm is used to partition the graph describing the voxel affinities within the striatum into the desired number of clusters. The method was experimentally validated with synthetic and real image data. The experiments showed that our method was able to automatically extract caudate, ventral striatum, and putamen from the images. Moreover, the putamen could be subdivided into anterior and posterior parts. An automatic method for the extraction of striatal structures from high-resolution PET images allows for inexpensive and reproducible extraction of the quantitative information from these images necessary in brain research and drug development

The Extension of the RAINS Model to Greenhouse Gases

Many of the traditional air pollutants and greenhouse gases have common sources, offering a cost-effective potential for simultaneous improvements for both traditional air pollution problems as well as climate change. A methodology has been developed to extend the RAINS integrated assessment model to explore synergies and trade-offs between the control of greenhouse gases and air pollution. With this extension, the RAINS model allows now the assessment of emission control costs for the six greenhouse gases covered under the Kyoto Protocol (CO2, CH4, N2O and the three F-gases) together with the emissions of air pollutants SO2, NOX, VOC, NH3 AND PM. In the first phase of the study, emissions, costs and control potentials for the six greenhouse gases covered in the Kyoto Protocol have been estimated and implemented in the RAINS model. Emission estimates are based on methodologies and emission factors proposed by the IPCC emission reporting guidelines. The large number of control options for greenhouse gases have been grouped into approximately 150 packages of measures and implemented in the RAINS model for the European countries. These control options span a wide range of cost-effectiveness. There a re certain advanced technical measures with moderate costs, and certain measures exist for which the economic assessment suggests even negative costs, if major side impacts (cost savings) are calculated. Illustrative example calculations clearly demonstrate that conclusions on the cost-effectiveness of emission reduction strategies are crucially depending on the boundaries of the analysis. The net cost of greenhouse gas control strategies are significantly lower if the immediate cost-savings from avoided air pollution control costs are taken into consideration. For a 15 percent reduction of the CO2 emissions from the power sector in the EU, avoided pollution control costs could compensate two third of the CO2 control costs. Depending on the design of the control strategy, net costs of greenhouse gas mitigation could even be negative, which is in stark contrast to conclusions for a CO2 only strategy. However, there are certain greenhouse gas mitigation measures, such as increased use of biomass that could deteriorate the negative impacts of air pollution, while yielding very little economic synergies. A combined approach towards greenhouse gas mitigation and air pollution control would not only reveal economic synergies, but also harness additional environmental benefits. Even in a situation with stringent emission control requirements for air pollution as it is required by the EU legislation, modifications in fuel use geared towards reductions of greenhouse gases could lead as a side impact to significant reductions in the residual emissions of air pollutants. The economic benefits of such "windfall emission reductions" could be substantial. The extended RAINS model framework will offer a tool to systematically investigate such economic and environmental synergies between greenhouse gas mitigation and air pollution control while avoiding negative side impacts

Steerable3D: An ImageJ plugin for neurovascular enhancement in 3-D segmentation

PurposeImage processing plays a fundamental role in the study of central nervous system, for example in the analysis of the vascular network in neurodegenerative diseases. Synchrotron X-ray Phase-contrast micro-Tomography (SXPCT) is a very attractive method to study weakly absorbing samples and features, such as the vascular network in the spinal cord (SC). However, the identification and segmentation of vascular structures in SXPCT images is seriously hampered by the presence of image noise and strong contrast inhomogeneities, due to the sensitivity of the technique to small electronic density variations. In order to help with these tasks, we implemented a user-friendly ImageJ plugin based on a 3D Gaussian steerable filter, tuned up for the enhancement of tubular structures in SXPCT images.MethodsThe developed 3D Gaussian steerable filter plugin for ImageJ is based on the steerability properties of Gaussian derivatives. We applied it to SXPCT images of ex-vivo mouse SCs acquired at different experimental conditions.ResultsThe filter response shows a strong amplification of the source image contrast-to-background ratio (CBR), independently of structures orientation. We found that after the filter application, the CBR ratio increases by a factor ranging from ~6 to ~60. In addition, we also observed an increase of 35% of the contrast to noise ratio in the case of injured mouse SC.ConclusionThe developed tool can generally facilitate the detection/segmentation of capillaries, veins and arteries that were not clearly observable in non-filtered SXPCT images. Its systematic application could allow obtaining quantitative information from pre-clinical and clinical images

Spatial based Expectation Maximizing (EM)

<p>Abstract</p> <p>Background</p> <p>Expectation maximizing (EM) is one of the common approaches for image segmentation.</p> <p>Methods</p> <p>an improvement of the EM algorithm is proposed and its effectiveness for MRI brain image segmentation is investigated. In order to improve EM performance, the proposed algorithms incorporates neighbourhood information into the clustering process. At first, average image is obtained as neighbourhood information and then it is incorporated in clustering process. Also, as an option, user-interaction is used to improve segmentation results. Simulated and real MR volumes are used to compare the efficiency of the proposed improvement with the existing neighbourhood based extension for EM and FCM.</p> <p>Results</p> <p>the findings show that the proposed algorithm produces higher similarity index.</p> <p>Conclusions</p> <p>experiments demonstrate the effectiveness of the proposed algorithm in compare to other existing algorithms on various noise levels.</p

Variation within the Huntington's Disease Gene Influences Normal Brain Structure

Genetics of the variability of normal and diseased brain structure largely remains to be elucidated. Expansions of certain trinucleotide repeats cause neurodegenerative disorders of which Huntington's disease constitutes the most common example. Here, we test the hypothesis that variation within the IT15 gene on chromosome 4, whose expansion causes Huntington's disease, influences normal human brain structure. In 278 normal subjects, we determined CAG repeat length within the IT15 gene on chromosome 4 and analyzed high-resolution T1-weighted magnetic resonance images by the use of voxel-based morphometry. We found an increase of GM with increasing long CAG repeat and its interaction with age within the pallidum, which is involved in Huntington's disease. Our study demonstrates that a certain trinucleotide repeat influences normal brain structure in humans. This result may have important implications for the understanding of both the healthy and diseased brain

Regional Grey Matter Structure Differences between Transsexuals and Healthy Controls-A Voxel Based Morphometry Study.

Gender identity disorder (GID) refers to transsexual individuals who feel that their assigned biological gender is incongruent with their gender identity and this cannot be explained by any physical intersex condition. There is growing scientific interest in the last decades in studying the neuroanatomy and brain functions of transsexual individuals to better understand both the neuroanatomical features of transsexualism and the background of gender identity. So far, results are inconclusive but in general, transsexualism has been associated with a distinct neuroanatomical pattern. Studies mainly focused on male to female (MTF) transsexuals and there is scarcity of data acquired on female to male (FTM) transsexuals. Thus, our aim was to analyze structural MRI data with voxel based morphometry (VBM) obtained from both FTM and MTF transsexuals (n = 17) and compare them to the data of 18 age matched healthy control subjects (both males and females). We found differences in the regional grey matter (GM) structure of transsexual compared with control subjects, independent from their biological gender, in the cerebellum, the left angular gyrus and in the left inferior parietal lobule. Additionally, our findings showed that in several brain areas, regarding their GM volume, transsexual subjects did not differ significantly from controls sharing their gender identity but were different from those sharing their biological gender (areas in the left and right precentral gyri, the left postcentral gyrus, the left posterior cingulate, precuneus and calcarinus, the right cuneus, the right fusiform, lingual, middle and inferior occipital, and inferior temporal gyri). These results support the notion that structural brain differences exist between transsexual and healthy control subjects and that majority of these structural differences are dependent on the biological gender

Contribution of astrocytes to familial risk and clinical manifestation of schizophrenia

Previous studies have implicated several brain cell types in schizophrenia (SCZ), but the genetic impact of astrocytes is unknown. Considering their high complexity in humans, astrocytes are likely key determinants of neurodevelopmental diseases, such as SCZ. Human induced pluripotent stem cell (hiPSC)-derived astrocytes differentiated from five monozygotic twin pairs discordant for SCZ and five healthy subjects were studied for alterations related to high genetic risk and clinical manifestation of SCZ in astrocyte transcriptomics, neuron-astrocyte co-cultures, and in humanized mice. We found gene expression and signaling pathway alterations related to synaptic dysfunction, inflammation, and extracellular matrix components in SCZ astrocytes, and demyelination in SCZ astrocyte transplanted mice. While Ingenuity Pathway Analysis identified SCZ disease and synaptic transmission pathway changes in SCZ astrocytes, the most consistent findings were related to collagen and cell adhesion associated pathways. Neuronal responses to glutamate and GABA differed between astrocytes from control persons, affected twins, and their unaffected co-twins and were normalized by clozapine treatment. SCZ astrocyte cell transplantation to the mouse forebrain caused gene expression changes in synaptic dysfunction and inflammation pathways of mouse brain cells and resulted in behavioral changes in cognitive and olfactory functions. Differentially expressed transcriptomes and signaling pathways related to synaptic functions, inflammation, and especially collagen and glycoprotein 6 pathways indicate abnormal extracellular matrix composition in the brain as one of the key characteristics in the etiology of SCZ.Peer reviewe