Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.

http://download.neurosky.com/docs/papers/bcq/Klimesch_1999_EEG_alpha_and_theta_oscillations_reflect_cognitive_and_memory_performance.pdf

EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

http://psych.colorado.edu/~kimlab/Botvinick.etal.TICS.2004.pdf

Conflict monitoring and anterior cingulate cortex: an update

EEG alpha oscillations: The inhibition–timing hypothesis

▪ Abstract Image segmentation plays a crucial role in many medical-imaging applications, by automating or facilitating the delineation of anatomical structures and other regions of interest. We present a critical appraisal of the current status of semiautomated and automated methods for the segmentation of anatomical medical images. Terminology and important issues in image segmentation are first presented. Current segmentation approaches are then reviewed with an emphasis on the advantages and disadvantages of these methods for medical imaging applications. We conclude with a discussion on the future of image segmentation methods in biomedical research.

Current methods in medical image segmentation.

Changes in cortical activity during working memory tasks were examined with electroencephalograms (EEGs) sampled from 115 channels and spatially sharpened with magnetic resonance imaging (MRI)-based finite element deblurring. Eight subjects performed tasks requiring comparison of each stimulus to a preceding one on verbal or spatial attributes. A frontal midline theta rhythm increased in magnitude with increased memory load. Dipole models localized this signal to the region of the anterior cingulate cortex. A slow (low-frequency), parietocentral, alpha signal decreased with increased working memory load. These signals were insensitive to the type of stimulus attribute being processed. A faster (higherfrequency), occipitoparietal, alpha signal was relatively attenuated in the spatial version of the task, especially over the posterior right hemisphere. Theta and alpha signals increased, and overt performance improved, after practice on the tasks. Increases in theta with both increased task difficulty and with practice suggests that focusing attention required more effort after an extended test session. Decreased alpha in the difficult tasks indicates that this signal is inversely related to the amount of cortical resources allocated to task performance. Practice-related increases in alpha suggest that fewer cortical resources are required after skill development. These results serve: (i) to dissociate the effects of task difficulty and practice; (ii) to differentiate the involvement of posterior cortex in spatial versus verbal tasks; (iii) to localize frontal midline theta to the anteromedial cortex; and (iv) to demonstrate the feasibility of using anatomical MRIs to remove the blurring effect of the skull and scalp from the ongoing EEG. The results are discussed with respect to those obtained in a prior study of transient evoked potentials during working memory.

/pdf/high-resolution-eeg-mapping-of-cortical-activation-related-rvtl6pja0p.pdf

High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice.

Reconstructing the geometry of the human cerebral cortex from MR images is an important step in both brain mapping and surgical path planning applications. Difficulties with imaging noise, partial volume averaging, image intensity inhomogeneities, convoluted cortical structures, and the requirement to preserve anatomical topology make the development of accurate automated algorithms particularly challenging. Here the authors address each of these problems and describe a systematic method for obtaining a surface representation of the geometric central layer of the human cerebral cortex. Using fuzzy segmentation, an isosurface algorithm, and a deformable surface model, the method reconstructs the entire cortex with the correct topology, including deep convoluted sulci and gyri. The method is largely automated and its results are robust to imaging noise, partial volume averaging, and image intensity inhomogeneities. The performance of this method is demonstrated, both qualitatively and quantitatively, and the results of its application to six subjects and one simulated MR brain volume are presented.

http://iacl.ece.jhu.edu/Pubs/p103j-ieee.pdf

Reconstruction of the human cerebral cortex from magnetic resonance images

Reconstruction of the human cerebral cortex from MR images is a fundamental step in human brain mapping and in applications such as surgical path planning. In a previous paper, we described a method for obtaining a surface representation of the central layer of the human cerebral cortex using fuzzy segmentation and a deformable surface model. This method, however, suffers from several problems. In this paper, we significantly improve upon the previous method by using a fuzzy segmentation algorithm robust to intensity inhomogeneities, and using a deformable surface model specifically designed for capturing convoluted sulci or gyri. We demonstrate the improvement over the previous method both qualitatively and quantitatively, and show the result of its application to six subjects. We also experimentally validate the convergence of the deformable surface initialization algorithm.

/pdf/reconstruction-of-the-central-layer-of-the-human-cerebral-3tdfv9qtmx.pdf

Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images

Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data. However, networks trained on synthetic data often fail to generalize to real data. Cinematic rendering simulates the propagation and interaction of light passing through tissue models reconstructed from CT data, enabling the generation of photorealistic images. In this paper, we present one of the first applications of cinematic rendering in deep learning, in which we propose to fine-tune synthetic data-driven networks using cinematically rendered CT data for the task of monocular depth estimation in endoscopy. Our experiments demonstrate that: (a) convolutional neural networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model. Our empirical evaluation demonstrates that networks fine-tuned with cinematically rendered data predict depth with 56.87% less error for rendered endoscopy images and 27.49% less error for real porcine colon endoscopy images.

Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images

A recognition pipeline automatically partitions a 3D image of the human body into regions of interest (head, rib cage, pelvis, and legs) and correctly labels each region. The 3D image is projected onto a 2D image using volume rendering. The 3D image may contain the whole body region or any subset. In a learning phase, training datasets are manually partitioned and labeled, and a training database is computed. In a recognition phase, images are partitioned and labeled based on the knowledge from the training database. The recognition phase is computationally efficient and may operate under 2 seconds in current conventional computer systems. The recognition is robust to image variations, and does not require the user to provide any knowledge about the contained regions of interest within the image.

Daphne Yu

Papers

High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice.

Reconstruction of the human cerebral cortex from magnetic resonance images

Reconstruction of the Central Layer of the Human Cerebral Cortex from MR Images

Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images

Automatic partitioning and recognition of human body regions from an arbitrary scan coverage image