Quantification of total fetal brain volume using 3D MR imaging data acquired in utero.

Objective: Interpretation of magnetic resonance (MR) imaging of the fetal brain in utero is primarily undertaken using 2D images to provide anatomical information about structural abnormalities. It is now possible to obtain 3D image acquisitions that allow measurement of fetal brain volumes that are potentially useful clinically. The aim of our current work is to provide reference values of total brain volumes obtained from a cohort of low risk fetuses with no abnormalities on ante-natal ultrasonography and in utero MR imaging. Method: Images from volume MR acquisitions of 132 fetuses were used to extract brain volumes by manual segmentation. Reproducibility and reliability were assessed by analysis of the results of two subgroups who had repeated measurements made by the primary and a secondary observer. Results: Intra-observer and inter-observer agreement was high with no statistically significant differences between and within observers (p = 0.476 and p = 0.427, respectively). The results of the brain volume assessments are presented graphically with mean and 95% prediction limits alongside estimates of normal growth rates. Conclusion: We have shown that fetal brain volumes can be reliably extracted from in utero MR (iuMR) imaging 3D datasets with a high degree of reproducibility. The resultant data could potentially be used as a reference tool in the clinical setting

Brain organoid data synthesis and evaluation

IntroductionDatasets containing only few images are common in the biomedical field. This poses a global challenge for the development of robust deep-learning analysis tools, which require a large number of images. Generative Adversarial Networks (GANs) are an increasingly used solution to expand small datasets, specifically in the biomedical domain. However, the validation of synthetic images by metrics is still controversial and psychovisual evaluations are time consuming.MethodsWe augment a small brain organoid bright-field database of 40 images using several GAN optimizations. We compare these synthetic images to the original dataset using similitude metrcis and we perform an psychovisual evaluation of the 240 images generated. Eight biological experts labeled the full dataset (280 images) as syntetic or natural using a custom-built software. We calculate the error rate per loss optimization as well as the hesitation time. We then compare these results to those provided by the similarity metrics. We test the psychovalidated images in a training step of a segmentation task.Results and discussionGenerated images are considered as natural as the original dataset, with no increase of the hesitation time by experts. Experts are particularly misled by perceptual and Wasserstein loss optimization. These optimizations render the most qualitative and similar images according to metrics to the original dataset. We do not observe a strong correlation but links between some metrics and psychovisual decision according to the kind of generation. Particular Blur metric combinations could maybe replace the psychovisual evaluation. Segmentation task which use the most psychovalidated images are the most accurate

Development of a psychiatric disorder linked to cerebellar lesions

Cerebellar dysfunction plays a critical role in neurodevelopmental disorders with long-term behavioral and neuropsychiatric symptoms. A 43-year-old woman with a cerebellum arteriovenous malformation and history of behavioral dysregulation since childhood is described. After the rupture of the cerebellar malformation in adulthood, her behavior morphed into specific psychiatric symptoms and cognitive deficits occurred. The neuropsychological assessment evidenced impaired performance in attention, visuospatial, memory, and language domains. Moreover, psychiatric assessment indicated a borderline personality disorder. Brain MRI examination detected macroscopic abnormalities in the cerebellar posterior lobules VI, VIIa (Crus I), and IX, and in the posterior area of the vermis, regions usually involved in cognitive and emotional processing. The described patient suffered from cognitive and behavioral symptoms that are part of the cerebellar cognitive affective syndrome. This case supports the hypothesis of a cerebellar role in personality disorders emphasizing the importance of also examining the cerebellum in the presence of behavioral disturbances in children and adults

Collaborative patch-based super-resolution for diffusion-weighted images

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquis itions. A comparison with classical interpo- lation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in termsofimprovementsonimagereconstruction,fractiona lanisotropy(FA)estimation,generalizedFAandangular reconstruction for tensor and high angular resolut ion diffusion imaging (HARDI) models. Besides, fi rst results of reconstructed ultra high resolution DW images are presented at 0.6 × 0.6 × 0.6 mm 3 and0.4×0.4×0.4mm 3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fi ber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.We thank the reviewers for their useful comments that helped improve the paper. We also want to thank the Pr Louis Collins for proofreading this paper and his fruitful comments. Finally, we want to thank Martine Bordessoules for her help during image acquisition of DWI used to build the phantom. This work has been supported by the French grant "HR-DTI" ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. This work has been also partially supported by the French National Agency for Research (Project MultImAD; ANR-09-MNPS-015-01) and by the Spanish grant TIN2011-26727 from the Ministerio de Ciencia e Innovacion. This work benefited from the use of FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), FiberNavigator (code.google.com/p/fibernavigator/), MRtrix software (http://www. brain.org.au/software/mrtrix/) and ITKsnap (www.itk.org).Coupé, P.; Manjón Herrera, JV.; Chamberland, M.; Descoteaux, M.; Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage. 83:245-261. https://doi.org/10.1016/j.neuroimage.2013.06.030S2452618

Значение мотивации персонала на предприятии

Основная цель – систематизировать сведения о мотивации персонала и его значении на предприятии

Prevalence of Prenatal Brain Abnormalities in Fetuses with Congenital Heart Disease: Systematic Review.

OBJECTIVES: Studies have demonstrated an association between congenital heart defects (CHD) and postnatal brain abnormalities and neurodevelopmental delay. Recent evidence suggests that some of these brain abnormalities are present even before birth. The primary aim of this study was to perform a systematic review to quantify the prevalence of prenatal brain abnormalities in fetuses with CHD. METHODS: MEDLINE, EMBASE and The Cochrane Library were searched. Reference lists within each article were hand-searched for additional reports. The outcomes included structural brain abnormalities (MRI), changes in brain volume (MRI, 3-D volumetric MRI, 3-D ultrasound and Phase Contrast Magnetic Resonance), metabolism or maturation (Magnetic Resonance Spectroscopy and Phase Contrast Magnetic Resonance) and blood flow (Doppler ultrasound, Phase Contrast Magnetic Resonance and 3D Power Doppler ultrasound) in fetuses with CHD. Cohort and case-control studies were included. Cases of chromosomal or genetic abnormalities, case reports and editorials were excluded. Proportion meta-analysis was used for analysis. Between-study heterogeneity was assessed using the I(2) test (Registration number: CRD42015025546). RESULTS: The search yielded 1,943 citations; and 20 studies were included in the review (n = 1175 cases, 221 in the meta-analysis). Three studies reported data on structural brain abnormalities, while data on altered brain volume, metabolism and blood flow were reported in 7, 3 and 14 studies, respectively. The three studies reporting data on structural brain abnormalities were suitable for inclusion in a meta-analysis (221 cases). The prevalence of prenatal structural brain abnormalities in fetuses with CHD was 28% (95% CI 18%-40%), similar prevalence in fetuses with tetralogy of Fallot of 25% (95% CI 14%-39). These abnormalities included ventriculomegaly (commonest), agenesis of the corpus callosum, ventricular bleeding, increased extra-axial space, vermian hypoplasia, white matter abnormalities and delayed brain development. Fetuses with CHD were more likely, than those without CHD, to have reduced brain volume, delay in brain maturation and altered brain circulation, most commonly in the form of reduced middle cerebral artery pulsatility index and cerebroplacental ratio. These changes are usually evident in the third trimester, but some studies have reported them as early as the second trimester. CONCLUSIONS: In the absence of known major aneuploidy or genetic syndromes, fetuses with CHD are at increased risk of brain abnormalities, which are present antenatally

Paramétrisation et parcellisation automatique de la surface corticale

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A general deep learning framework for neuron instance segmentation based on Efficient UNet and morphological post-processing

Recent studies have demonstrated the superiority of deep learning in medical image analysis, especially in cell instance segmentation, a fundamental step for many biological studies. However, the excellent performance of the neural networks requires training on large, unbiased dataset and annotations, which is labor-intensive and expertise-demanding. This paper presents an end-to-end framework to automatically detect and segment NeuN stained neuronal cells on histological images using only point annotations. Unlike traditional nuclei segmentation with point annotation, we propose using point annotation and binary segmentation to synthesize pixel-level annotations. The synthetic masks are used as the ground truth to train the neural network, a U-Net-like architecture with a stateof-the-art network, EfficientNet, as the encoder. Validation results show the superiority of our model compared to other recent methods. In addition, we investigated multiple post-processing schemes and proposed an original strategy to convert the probability map into segmented instances using ultimate erosion and dynamic reconstruction. This approach is easy to configure and outperforms other classical post-processing techniques. This work aims to develop a robust and efficient framework for analyzing neurons using optical microscopic data, which can be used in preclinical biological studies and, more specifically, in the context of neurodegenerative diseases

TDA-Clustering Strategies for the Characterization of Brain Organoids

International audienceWe propose to use Topological Data Analysis (TDA) to in-tent deciphering the morphological development of these cultures seg-mented with U-Net. To classify various shape at three developmentalstages, we propose to combine TDA with a Kmean Clustering or with aSupport Vector Machine. We calculated some characteristics as regres-sions on the rendered presentations to compare mean representationsfrom each stage. Results show a specific morphological pattern (whateverthe kind of TDA clustering) appears between 9 and 14 days correspond-ing to the neuroepithelial formations which has to be further studied andvalidated on an other dataset. Hence, our method provides an indicatorof neuroepithelial growth formation stage prediction in brain organoids,it has to be compared with others classification methodologies