scholarly journals Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guangjun Zhao ◽  
Xuchu Wang ◽  
Yanmin Niu ◽  
Liwen Tan ◽  
Shao-Xiang Zhang
Keyword(s):  
High Resolution ◽  
Human Brain ◽  
Critical Role ◽  
Feature Representation ◽  
Imaging Data ◽  
Anatomical Structures ◽  
Stacked Autoencoder ◽  
Chinese Visible Human ◽  
Visible Human ◽  
Brain Tissues

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

scholarly journals High-Resolution Neural Network for Driver Visual Attention Prediction

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2030 ◽  
Author(s):  
Byeongkeun Kang ◽  
Yeejin Lee
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Visual Attention ◽  
Visual Information ◽  
Prediction Accuracy ◽  
Critical Role ◽  
Image Resolution ◽  
Feature Representation ◽  
Safe Driving ◽  
Feature Representations

Driving is a task that puts heavy demands on visual information, thereby the human visual system plays a critical role in making proper decisions for safe driving. Understanding a driver’s visual attention and relevant behavior information is a challenging but essential task in advanced driver-assistance systems (ADAS) and efficient autonomous vehicles (AV). Specifically, robust prediction of a driver’s attention from images could be a crucial key to assist intelligent vehicle systems where a self-driving car is required to move safely interacting with the surrounding environment. Thus, in this paper, we investigate a human driver’s visual behavior in terms of computer vision to estimate the driver’s attention locations in images. First, we show that feature representations at high resolution improves visual attention prediction accuracy and localization performance when being fused with features at low-resolution. To demonstrate this, we employ a deep convolutional neural network framework that learns and extracts feature representations at multiple resolutions. In particular, the network maintains the feature representation with the highest resolution at the original image resolution. Second, attention prediction tends to be biased toward centers of images when neural networks are trained using typical visual attention datasets. To avoid overfitting to the center-biased solution, the network is trained using diverse regions of images. Finally, the experimental results verify that our proposed framework improves the prediction accuracy of a driver’s attention locations.

Furosemide and Mannitol Suppression of Epileptic Activity in the Human Brain

2005 ◽  
Vol 94 (2) ◽  
pp. 907-918 ◽  
Author(s):  
Michael M. Haglund ◽  
Daryl W. Hochman
Keyword(s):  
Electrical Stimulation ◽  
Human Brain ◽  
Optical Imaging ◽  
Neuronal Excitability ◽  
Critical Role ◽  
Intractable Epilepsy ◽  
Mesial Temporal Lobe Epilepsy ◽  
Epileptic Activity ◽  
Imaging Data ◽  
Epileptiform Discharges

Most research on basic mechanisms of epilepsy and the design of new antiepileptic drugs has focused on synaptic transmission or action potential generation. However, a number of laboratory studies have suggested that nonsynaptic mechanisms, such as modulation of electric field interactions via the extracellular space (ECS), might also contribute to neuronal hypersynchrony and epileptogenicity. To date, a role for nonsynaptic modulation of epileptic activity in the human brain has not been investigated. Here we studied the effects of molecules that modulate the volume and water content of the ECS on epileptic activity in patients suffering from neocortical and mesial temporal lobe epilepsy. Electrophysiological and optical imaging data were acquired from the exposed cortices of anesthetized patients undergoing surgical treatment for intractable epilepsy. Patients were given a single intravenous injection containing either 20 mg furosemide (a cation-chloride cotransporter antagonist) or 50 g mannitol (an osmolyte). Furosemide and mannitol both significantly suppressed spontaneous epileptic spikes and electrical stimulation-evoked epileptiform discharges in all subjects, completely blocking all epileptic activity in some patients without suppressing normal electroencephalographic activity. Optical imaging suggested that the spread of electrical stimulation-evoked activity over the cortex was significantly reduced by these treatments, but the magnitude of neuronal activation near the stimulating electrode was not diminished. These results suggest that nonsynaptic mechanisms play a critical role in modulating the epileptogenicity of the human brain. Furosemide and other drugs that modulate the ECS might possess clinically useful antiepileptic properties, while avoiding the side effects associated with the suppression of neuronal excitability.

A Digital Interactive Human Brain Atlas Based on Chinese Visible Human Datasets for Anatomy Teaching

2014 ◽  
Vol 25 (1) ◽  
pp. 303-307 ◽  
Author(s):  
Qiyu Li ◽  
Xu Ran ◽  
Shaoxiang Zhang ◽  
Liwen Tan ◽  
Mingguo Qiu
Keyword(s):  
Human Brain ◽  
Brain Atlas ◽  
Chinese Visible Human ◽  
Anatomy Teaching ◽  
Visible Human

Construction of a registration and fusion of unreformed thin-sectional high-resolution sectional anatomical image (Chinese Visible Human images) and CT and MRI images based on B-spline and mutual information and its application in segmenting nasopharyngeal structures in Treatment Planning System (TPS)

2020 ◽  
Author(s):  
Jingyi Yang ◽  
Xiaoqin Zhang ◽  
Bangyu Luo ◽  
Hongjun Liu ◽  
Zhou Xu ◽  
...  
Keyword(s):  
High Resolution ◽  
Mutual Information ◽  
Treatment Planning ◽  
Teaching Quality ◽  
Ct Images ◽  
Planning System ◽  
Treatment Planning System ◽  
B Spline ◽  
Chinese Visible Human ◽  
Visible Human

Abstract Background To help radiotherapy doctors recognize and segment the nasopharyngeal organs in risk of Nasopharyngeal carcinoma (NPC) and make radiotherapy plan. Materials/Methods: Based on the continuous thin-layer, high-precision, high-resolution and true-color sectional anatomical data (Chinese Visible Human (CVH) images),we used B-spline and mutual information to transform, register and fuse the CVH images with the patient's personalized CT images, and integrated them into the Treatment Planning System (TPS). Consequently, Three-Dimensional Visualization Treatment Planning System (3DV + TPS) was created. To verify it, 3DV + TPS was deployed to identify and segment the nasopharyngeal organs in risk of NPC, and a questionnaire was filled out by radiotherapy doctors. Results Result shows that 3DV + TPS can finish registration and fusion of 4 sets of sectional anatomical images and individual CT images of patients in approximately 3 minutes and 50 seconds. Conclusion The registered and fused images can accurately reflect the position, outline and adjacent space of the nasopharyngeal structure which is not clear in the CT images. Thus, it is helpful for recognizing and segmenting neural, muscular and glandular structures. Through automatically registering and fusing of color images and CT gray images, 3DV + TPS improves the accuracy and efficiency of recognizing nasopharyngeal structures in making radiotherapy plan, and it is useful to improve the teaching quality of tumor radiotherapy for medical students and interns as well.

Three-dimensional imaging of human brain tissues using absorption-contrast high-resolution X-ray tomography

2017 ◽  
Author(s):  
Anna Khimchenko ◽  
Georg Schulz ◽  
Christos Bikis ◽  
Hans Deyhle ◽  
Natalia Chicherova ◽  
...  
Keyword(s):  
High Resolution ◽  
Human Brain ◽  
Three Dimensional ◽  
Three Dimensional Imaging ◽  
X Ray ◽  
Absorption Contrast ◽  
Brain Tissues

scholarly journals Application of Airy beam light sheet microscopy to examine early neurodevelopmental structures in 3D hiPSC-derived human cortical spheroids

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dwaipayan Adhya ◽  
George Chennell ◽  
James A. Crowe ◽  
Eva P. Valencia-Alarcón ◽  
James Seyforth ◽  
...  
Keyword(s):  
High Resolution ◽  
Human Brain ◽  
Light Sheet ◽  
Autism Spectrum ◽  
Model Systems ◽  
Large Field ◽  
Autism Spectrum Conditions ◽  
Patient Specific ◽  
Airy Beam

Abstract Background The inability to observe relevant biological processes in vivo significantly restricts human neurodevelopmental research. Advances in appropriate in vitro model systems, including patient-specific human brain organoids and human cortical spheroids (hCSs), offer a pragmatic solution to this issue. In particular, hCSs are an accessible method for generating homogenous organoids of dorsal telencephalic fate, which recapitulate key aspects of human corticogenesis, including the formation of neural rosettes—in vitro correlates of the neural tube. These neurogenic niches give rise to neural progenitors that subsequently differentiate into neurons. Studies differentiating induced pluripotent stem cells (hiPSCs) in 2D have linked atypical formation of neural rosettes with neurodevelopmental disorders such as autism spectrum conditions. Thus far, however, conventional methods of tissue preparation in this field limit the ability to image these structures in three-dimensions within intact hCS or other 3D preparations. To overcome this limitation, we have sought to optimise a methodological approach to process hCSs to maximise the utility of a novel Airy-beam light sheet microscope (ALSM) to acquire high resolution volumetric images of internal structures within hCS representative of early developmental time points. Results Conventional approaches to imaging hCS by confocal microscopy were limited in their ability to image effectively into intact spheroids. Conversely, volumetric acquisition by ALSM offered superior imaging through intact, non-clarified, in vitro tissues, in both speed and resolution when compared to conventional confocal imaging systems. Furthermore, optimised immunohistochemistry and optical clearing of hCSs afforded improved imaging at depth. This permitted visualization of the morphology of the inner lumen of neural rosettes. Conclusion We present an optimized methodology that takes advantage of an ALSM system that can rapidly image intact 3D brain organoids at high resolution while retaining a large field of view. This imaging modality can be applied to both non-cleared and cleared in vitro human brain spheroids derived from hiPSCs for precise examination of their internal 3D structures. This process represents a rapid, highly efficient method to examine and quantify in 3D the formation of key structures required for the coordination of neurodevelopmental processes in both health and disease states. We posit that this approach would facilitate investigation of human neurodevelopmental processes in vitro.

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