scholarly journals Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG

2020 ◽  
Vol 14 ◽  
Author(s):  
Abhishek S. Bhutada ◽  
Pradyumna Sepúlveda ◽  
Rafael Torres ◽  
Tomás Ossandón ◽  
Sergio Ruiz ◽  
...  
Keyword(s):  
Spatial Information ◽  
Three Dimensional ◽  
Spatial Localization ◽  
Accurate Identification ◽  
Brain Scans ◽  
3D Space ◽  
Automated Method ◽  
Simultaneous Acquisition ◽  
Additional Equipment ◽  
Magnetic Resonance Imaging Mri

Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

scholarly journals Three-Dimensional Convolutional Autoencoder Extracts Features of Structural Brain Images With a “Diagnostic Label-Free” Approach: Application to Schizophrenia Datasets

2021 ◽  
Vol 15 ◽  
Author(s):  
Hiroyuki Yamaguchi ◽  
Yuki Hashimoto ◽  
Genichi Sugihara ◽  
Jun Miyata ◽  
Toshiya Murai ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Brain Imaging ◽  
Spatial Information ◽  
Three Dimensional ◽  
Clinical Information ◽  
Label Free ◽  
Brain Images ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

There has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (3D-CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS) and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets, including 71 SZ patients and 71 HS. We created 16 3D-CAE models with different channels and convolutions to explore the effective range of hyperparameters for psychiatric brain imaging. The number of blocks containing two convolutional layers and one pooling layer was set, ranging from 1 block to 4 blocks. The number of channels in the extraction layer varied from 1, 4, 16, and 32 channels. The proposed 3D-CAEs were successfully reproduced into 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relation to clinical information. We explored the appropriate hyperparameter range of 3D-CAE, and it was suggested that a model with 3 blocks may be related to extracting features for predicting the dose of medication and symptom severity in schizophrenia.

High-resolution MRI of the ulnar and radial collateral ligaments of the wrist

2017 ◽  
Vol 58 (12) ◽  
pp. 1493-1499 ◽  
Author(s):  
Taiki Nozaki ◽  
Wei Der Wu ◽  
Yasuhito Kaneko ◽  
Gregory Rafijah ◽  
Lily Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Three Dimensional ◽  
Collateral Ligaments ◽  
Ulnar Styloid ◽  
Accurate Identification ◽  
3D Images ◽  
Observer Reliability ◽  
High Resolution Mri ◽  
Magnetic Resonance Imaging Mri ◽  
3D Magnetic Resonance Imaging

Background Accurate diagnosis of injuries to the collateral ligaments of the wrist is technically challenging on MRI. Purpose To investigate usefulness of high-resolution two-dimensional (2D) and isotropic three-dimensional (3D) magnetic resonance imaging (MRI) for identifying and classifying the morphology of the ulnar and radial collateral ligaments (UCL and RCL) of the wrist. Material and Methods Thirty-seven participants were evaluated using 3T coronal 2D and isotropic 3D images by two radiologists independently. The UCL was classified into four types: 1a, narrow attachment to the tip of the ulnar styloid (Tip); 1b, broad attachment to the Tip; 2a, narrow attachment to the medial base of the ulnar styloid (Base); and 2b, broad attachment to the Base. The RCL was also classified into four types: 1a, separate radioscaphoid and scaphotrapezial ligaments (RS + ST) with narrow scaphoid attachment; 1b, RS + ST with broad scaphoid attachment; 2a, continuous radio-scapho-trapezial ligaments (RST) with narrow scaphoid attachment; and 2b, RST with broad scaphoid attachment. The inter-observer reliability of these classifications was calculated. Results Type 1a was the most common of both collateral ligaments. Of UCL classifications, 31.4% were revised after additional review of multiplanar reconstruction (MPR) images from isotropic data. The inter-observer reliability of UCL classification was substantial (k = 0.62) without MPR, and almost perfect (k = 0.84) with MPR. The inter-observer reliability of RCL classification was almost perfect (k = 0.89). Anatomic delineation between the two sequences was not statistically different. Conclusion The UCL and RCL were each identified on high-resolution 2D and isotropic 3D MRI equally well. MPR allows accurate identification of the UCL attachment to the ulnar styloid.

scholarly journals Three-dimensional convolutional autoencoder extracts features of structural brain images with a “diagnostic label-free” approach: Application to schizophrenia datasets

2020 ◽  
Author(s):  
Hiroyuki Yamaguchi ◽  
Yuki Hashimoto ◽  
Genichi Sugihara ◽  
Jun Miyata ◽  
Toshiya Murai ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Spatial Information ◽  
Three Dimensional ◽  
Clinical Information ◽  
Label Free ◽  
Brain Images ◽  
Diagnostic Labels ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

ABSTRACTThere has been increasing interest in performing psychiatric brain imaging studies using deep learning. However, most studies in this field disregard three-dimensional (3D) spatial information and targeted disease discrimination, without considering the genetic and clinical heterogeneity of psychiatric disorders. The purpose of this study was to investigate the efficacy of a 3D convolutional autoencoder (CAE) for extracting features related to psychiatric disorders without diagnostic labels. The network was trained using a Kyoto University dataset including 82 patients with schizophrenia (SZ) and 90 healthy subjects (HS), and was evaluated using Center for Biomedical Research Excellence (COBRE) datasets including 71 SZ patients and 71 HS. The proposed 3D-CAEs were successfully reconstructed into high-resolution 3D structural magnetic resonance imaging (MRI) scans with sufficiently low errors. In addition, the features extracted using 3D-CAE retained the relevant clinical information. We explored the appropriate hyper parameter range of 3D-CAE, and it was suggested that a model with eight convolution layers might be relevant to extract features for predicting the dose of medication and symptom severity in schizophrenia.

Computed Tomography and Magnetic Resonance Imaging-aided Diagnosis of Primary Essential Cutis Verticis Gyrata: A Case Report with 5-year Follow-up and Review of the Literature

2019 ◽  
Vol 15 (9) ◽  
pp. 906-910
Author(s):  
Hongzhang Zhu ◽  
Shi-Ting Feng ◽  
Xingqi Zhang ◽  
Zunfu Ke ◽  
Ruixi Zeng ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Three Dimensional ◽  
Stable Condition ◽  
Resonance Imaging ◽  
Mri Findings ◽  
Cutis Verticis Gyrata ◽  
Essential Form ◽  
History Of ◽  
Magnetic Resonance Imaging Mri

Background: Cutis Verticis Gyrata (CVG) is a rare skin disease caused by overgrowth of the scalp, presenting as cerebriform folds and wrinkles. CVG can be classified into two forms: primary (essential and non-essential) and secondary. The primary non-essential form is often associated with neurological and ophthalmological abnormalities, while the primary essential form occurs without associated comorbidities. Discussion: We report on a rare case of primary essential CVG with a 4-year history of normal-colored scalp skin mass in the parietal-occipital region without symptom in a 34-year-old male patient, retrospectively summarizing his pathological and Computer Tomography (CT) and magnetic resonance imaging (MRI) findings. The major clinical observations on the CT and MR sectional images include a thickened dermis and excessive growth of the scalp, forming the characteristic scalp folds. With the help of CT and MRI Three-dimensional (3D) reconstruction techniques, the characteristic skin changes could be displayed intuitively, providing more evidence for a diagnosis of CVG. At the 5-year followup, there were no obvious changes in the lesion. Conclusion: Based on our observations, we propose that not all patients with primary essential CVG need surgical intervention, and continuous clinical observation should be an appropriate therapy for those in stable condition.

scholarly journals Design, Fabrication, and Testing of a Novel 3D 3-Fingered Electrothermal Microgripper with Multiple Degrees of Freedom

Micromachines ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 444
Author(s):  
Guoning Si ◽  
Liangying Sun ◽  
Zhuo Zhang ◽  
Xuping Zhang
Keyword(s):  
Degrees Of Freedom ◽  
Dynamic Properties ◽  
Three Dimensional ◽  
Polyimide Film ◽  
Zebrafish Embryos ◽  
Multiple Degrees Of Freedom ◽  
Electrothermal Actuator ◽  
3D Space ◽  
Pick And Place

This paper presents the design, fabrication, and testing of a novel three-dimensional (3D) three-fingered electrothermal microgripper with multiple degrees of freedom (multi DOFs). Each finger of the microgripper is composed of a V-shaped electrothermal actuator providing one DOF, and a 3D U-shaped electrothermal actuator offering two DOFs in the plane perpendicular to the movement of the V-shaped actuator. As a result, each finger possesses 3D mobilities with three DOFs. Each beam of the actuators is heated externally with the polyimide film. The durability of the polyimide film is tested under different voltages. The static and dynamic properties of the finger are also tested. Experiments show that not only can the microgripper pick and place microobjects, such as micro balls and even highly deformable zebrafish embryos, but can also rotate them in 3D space.

scholarly journals Three-dimensional segmentation and depth-encoded visualization of choroidal vasculature using swept-source optical coherence tomography

2021 ◽  
pp. 153537022110285
Author(s):  
Hao Zhou ◽  
Tommaso Bacci ◽  
K Bailey Freund ◽  
Ruikang K Wang
Keyword(s):  
Optical Coherence Tomography ◽  
Three Dimensional ◽  
Age Related Macular Degeneration ◽  
Optical Coherence ◽  
Full Thickness ◽  
Swept Source ◽  
Automated Method ◽  
Choroidal Imaging ◽  
Choroidal Vasculature ◽  
Choroidal Vessels

The choroid provides nutritional support for the retinal pigment epithelium and photoreceptors. Choroidal dysfunction plays a major role in several of the most important causes of vision loss including age-related macular degeneration, myopic degeneration, and pachychoroid diseases such as central serous chorioretinopathy and polypoidal choroidal vasculopathy. We describe an imaging technique using depth-resolved swept-source optical coherence tomography (SS-OCT) that provides full-thickness three-dimensional (3D) visualization of choroidal anatomy including topographical features of individual vessels. Enrolled subjects with different clinical manifestations within the pachychoroid disease spectrum underwent 15 mm × 9 mm volume scans centered on the fovea. A fully automated method segmented the choroidal vessels using their hyporeflective lumens. Binarized choroidal vessels were rendered in a 3D viewer as a vascular network within a choroidal slab. The network of choroidal vessels was color depth-encoded with a reference to the Bruch’s membrane segmentation. Topographical features of the choroidal vasculature were characterized and compared with choroidal imaging obtained with indocyanine green angiography (ICGA) from the same subject. The en face SS-OCT projections of the larger choroid vessels closely resembled to that obtained with ICGA, with the automated SS-OCT approach proving additional depth-encoded 3D information. In 16 eyes with pachychoroid disease, the SS-OCT approach added clinically relevant structural details, including choroidal thickness and vessel depth, which the ICGA studies could not provide. Our technique appears to advance the in vivo visualization of the full-thickness choroid, successfully reveals the topographical features of choroidal vasculature, and shows potential for further quantitative analysis when compared with other choroidal imaging techniques. This improved visualization of choroidal vasculature and its 3D structure should provide an insight into choroid-related disease mechanisms as well as their responses to treatment.

scholarly journals Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2670
Author(s):  
Thomas Quirin ◽  
Corentin Féry ◽  
Dorian Vogel ◽  
Céline Vergne ◽  
Mathieu Sarracanie ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Mean Absolute Error ◽  
Tracking System ◽  
Three Dimensional ◽  
Absolute Error ◽  
Magnetic Tracking ◽  
Magnetic Camera ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.

scholarly journals A Four-point three-dimensional spatial localization algorithm based on RSSI

2020 ◽  
Vol 1550 ◽  
pp. 032022
Author(s):  
Li Ma ◽  
Ning Cao ◽  
Minghe Mao ◽  
Jianping Zhang
Keyword(s):  
Three Dimensional ◽  
Spatial Localization ◽  
Localization Algorithm

scholarly journals Virtual Archaeology of Death and Burial: A Procedure for Integrating 3D Visualization and Analysis in Archaeothanatology

2021 ◽  
Vol 7 (1) ◽  
pp. 540-555
Author(s):  
Hayley L. Mickleburgh ◽  
Liv Nilsson Stutz ◽  
Harry Fokkens
Keyword(s):  
Spatial Information ◽  
3D Visualization ◽  
Rapid Development ◽  
Three Dimensional ◽  
3D Models ◽  
The Body ◽  
Test Case ◽  
Archaeology Of Death ◽  
Human Burials ◽  
3D Information

Abstract The reconstruction of past mortuary rituals and practices increasingly incorporates analysis of the taphonomic history of the grave and buried body, using the framework provided by archaeothanatology. Archaeothanatological analysis relies on interpretation of the three-dimensional (3D) relationship of bones within the grave and traditionally depends on elaborate written descriptions and two-dimensional (2D) images of the remains during excavation to capture this spatial information. With the rapid development of inexpensive 3D tools, digital replicas (3D models) are now commonly available to preserve 3D information on human burials during excavation. A procedure developed using a test case to enhance archaeothanatological analysis and improve post-excavation analysis of human burials is described. Beyond preservation of static spatial information, 3D visualization techniques can be used in archaeothanatology to reconstruct the spatial displacement of bones over time, from deposition of the body to excavation of the skeletonized remains. The purpose of the procedure is to produce 3D simulations to visualize and test archaeothanatological hypotheses, thereby augmenting traditional archaeothanatological analysis. We illustrate our approach with the reconstruction of mortuary practices and burial taphonomy of a Bell Beaker burial from the site of Oostwoud-Tuithoorn, West-Frisia, the Netherlands. This case study was selected as the test case because of its relatively complete context information. The test case shows the potential for application of the procedure to older 2D field documentation, even when the amount and detail of documentation is less than ideal.

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