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

The hippocampus is a brain structure that is involved in several cognitive functions such as memory and learning. It is a structure of great interest due to its relationship to neurodegenerative processes such as the Alzheimer’s disease. In this work, we propose a novel multispectral multiatlas patch-based method to automatically segment hippocampus subfields using high resolution T1-weighted and T2-weighted magnetic resonance images (MRI). The proposed method works well also on standard resolution images after superresolution and consistently performs better than monospectral version. Finally, the proposed method was compared with similar state-of-the-art methods showing better results in terms of both accuracy and efficiency.

/pdf/high-resolution-hippocampus-subfield-segmentation-using-408yaqg211.pdf

High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion

Background
The posterior cerebellar lobules seem to be the anatomical substrate of cognitive cerebellar processes, but their microstructural alterations in multiple sclerosis (MS) remain unclear.
Objectives
To correlate diffusion metrics in lobules VI to VIIIb in persons with clinically isolated syn- drome (PwCIS) and in cognitively impaired persons with MS (CIPwMS) with their cognitive performances.
Methods
Sixty-nine patients (37 PwCIS, 32 CIPwMS) and 36 matched healthy subjects (HS) under- went 3T magnetic resonance imaging, including 3D T1-weighted and diffusion tensor imag- ing (DTI). Fractional anisotropy (FA) and mean diffusivity (MD) were calculated within each lobule and in the cerebellar peduncles. We investigated the correlations between cognitive outcomes and the diffusion parameters of cerebellar sub-structures and performed multiple linear regression analysis to predict cognitive disability.
Results
FA was generally lower and MD was higher in the cerebellum and specifically in the vermis Crus II, lobules VIIb and VIIIb in CIPwMS compared with PwCIS and HS. In hierarchical regression analyses, 31% of the working memory z score variance was explained by FA in the left lobule VI and in the left superior peduncle. Working memory was also associated with MD in the vermis Crus II. FA in the left lobule VI and right VIIIa predicted part of the information processing speed (IPS) z scores.
Conclusion
DTI indicators of cerebellar microstructural damage were associated with cognitive deficits in MS. Our results suggested that cerebellar lobular alterations have an impact on attention, working memory and IPS.

/pdf/microstructural-analyses-of-the-posterior-cerebellar-lobules-puuffbsqve.pdf

Microstructural analyses of the posterior cerebellar lobules in relapsing-onset multiple sclerosis and their implication in cognitive impairment.

Acoustic shadows appear in ultrasound images as regions of low signal intensity after boundaries with very high acoustic impedance differences. Acoustic shadows can be viewed as informative features to detect lesions or calcifications, or can be considered as damageable artifacts for image processing tasks such as segmentation, registration or 3D reconstruction. In both cases, the detection of these acoustic shadows is useful. This paper proposes a new geometrical method to detect these shadows based on statistical analysis of intensity profiles along the lines that compose the B-scan image. The results demonstrate that this detection improves the accuracy of 3D reconstruction of intraoperative ultrasound.

/pdf/acoustic-shadows-detection-application-to-accurate-u9uxstuuh8.pdf

Acoustic shadows detection, application to accurate reconstruction of 3D intraoperative ultrasound

This paper presents a new automatic segmentation scheme that combines active appearance (AAM) modeling and patch-based label fusion into a segmentation framework. AAM, which uses eigen-decomposition to analyze the statistical variation of image intensity and shape infor- mation over the population, is used to capture the global shape char- acteristics of the structure of interest with a generative model, while patch-based label fusion, which uses a non-local means method to com- pare the image local intensity properties, is applied to locally refine the segmentation results along the structure boundary area to improve the segmentation accuracy. The proposed segmentation scheme is used to segment human medial temporal lobe structures, which have low inten- sity contrast in MRI and complexity in shape. The experiments demon- strate that this new segmentation scheme is computationally efficient and robust. In a leave-one out validation with fifty-four normal young subjects, the method yields a mean Dice κ of 0.87 for hippocampus, 0.81 for amygdala, 0.73 for para-hippocampal complex cortex, and 0.73 for perirhinal cortex between manual and automatic labels.

https://hal.archives-ouvertes.fr/hal-00614308/document

Validation of appearance-model based segmentation with patch-based refinement on medial temporal lobe structures

Patch-Based Techniques in Medical Imaging : 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings

Pierrick Coupé

Papers

High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion

Microstructural analyses of the posterior cerebellar lobules in relapsing-onset multiple sclerosis and their implication in cognitive impairment.

Acoustic shadows detection, application to accurate reconstruction of 3D intraoperative ultrasound

Validation of appearance-model based segmentation with patch-based refinement on medial temporal lobe structures

Patch-Based Techniques in Medical Imaging : 4th International Workshop, Patch-MI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings