Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

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)

https://wiki.biac.duke.edu/lib/exe/fetch.php?media=biac:jones_etal_2012_the_do_s_and_don_ts_of_diffusion_mri.pdf

White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

Thank you very much for reading statistical parametric mapping the analysis of functional brain images. As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they cope with some infectious bugs inside their desktop computer.

Statistical Parametric Mapping The Analysis Of Functional Brain Images

Microstructural changes after an ischemic stroke (IS) have mainly been described in white matter. Data evaluating microstructural changes in gray matter (GM) remain scarce. The aim of the present study was to evaluate the integrity of GM on longitudinal data using mean diffusivity (MD), and its influence on post-IS cognitive performances. A prospective study was conducted, including supra-tentorial IS patients without pre-stroke disability. A cognitive assessment was performed at baseline and 1 year, including a Montreal Cognitive Assessment, an Isaacs set test, and a Zazzo cancelation task (ZCT): completion time and number of errors. A 3-T brain MRI was performed at the same two time-points, including diffusion tensor imaging for the assessment of GM MD. GM volume was also computed, and changes in GM volume and GM MD were evaluated, followed by the assessment of the relationship between these structural changes and changes in cognitive performances. One hundred and four patients were included (age 68.5 ± 21.5, 38.5% female). While no GM volume loss was observed, GM MD increased between baseline and 1 year. The increase of GM MD in left fronto-temporal regions (dorsolateral prefrontal cortex, superior and medial temporal gyrus, p < 0.05, Threshold-Free Cluster Enhancement, 5000 permutations) was associated with an increase time to complete ZCT, regardless of demographic confounders, IS volume and location, GM, and white matter hyperintensity volume. GM integrity deterioration was thus associated with processing speed slowdown, and appears to be a biomarker of cognitive frailty. This broadens the knowledge of post-IS cognitive impairment mechanisms. 

https://hal.science/hal-03651486/document

Microstructural Gray Matter Integrity Deteriorates After an Ischemic Stroke and Is Associated with Processing Speed

The goal of the current study was to introduce a new methodology that holds a promise to be used in hippocampus-aging studies using sub-millimeter super-resolution hybrid diffusion imaging (HYDI) MRI.HYDI diffusion data were acquired in two groups of older and younger healthy participants recruited from the Indiana Alzheimer's Disease Research Center and community. These data were then transformed into super-resolution diffusion images before the hippocampal subfield analyses. We studied the correlation between the subjects' age and the structural connectivity involving the hippocampal subfields and the connectivity between the whole hippocampus and the cerebral cortex.Structural integrity derived from the tractography streamlines between the hippocampal subfields was reduced in older than younger adults.The findings offered a new promising framework, and they opened avenues for future studies to explore the relationship between the structural connectivity in the hippocampal area and different types of dementia. 

/pdf/structural-connectivity-mapping-in-human-hippocampal-1k4rzzba.pdf

Structural connectivity mapping in human hippocampal-subfields using super-resolution hybrid diffusion imaging: a feasibility study

An image processing device comprises a store (110) for receiving image data (8) with a pixel identifier and at least one piece of associated intensity data and a pilot (100) which is provided to call a reduction unit (120) with pixels to be weighted and, for each pixel, in order to define a first neighborhood and a plurality of second neighborhoods with a neighborhood selection unit (130), in order to obtain a weighting value for the second neighborhood with a weighting unit (140) with the first neighborhood and a neighborhood among the plurality of second neighborhoods, in order to define each time a piece of weighted intensity data of a working pixel as the weighting value of the second neighborhood multiplied by the intensity data of the associated working pixel, and in order to define a piece of weighted intensity data of the pixel to be weighted by adding each of the pieces of weighted intensity data of a working pixel The weighting unit (140) is provided to calculate the distance between first and second neighborhoods on the basis of a sum, where each square of the difference between the respective intensity data of these neighborhoods is modulated by a quantity which is inversely proportional to the intensity data in the second neighborhood

Improved device for processing images

Prediction of MCI converters in the ADNI cohort using patterns of cortical thinning

Track-Density Imaging (TDI) has been proposed as super-resolution method for diffusion-weighted images (DWI). Based on fiber-tracking (FT), TDI enables to produce high-resolution white matter images. However, the sensitivity to noise of FT negatively impacts TDI accuracy and reproducibility. In this study, we analyzed whether the use of DWI denoising is able to improve TDI robustness and reproducibility.

https://hal.archives-ouvertes.fr/hal-00961150/document

Pierrick Coupé

Papers

Microstructural Gray Matter Integrity Deteriorates After an Ischemic Stroke and Is Associated with Processing Speed

Structural connectivity mapping in human hippocampal-subfields using super-resolution hybrid diffusion imaging: a feasibility study

Improved device for processing images

Prediction of MCI converters in the ADNI cohort using patterns of cortical thinning

Impact of DWI denoising on Track-Density Imaging