scholarly journals Automated skull stripping in mouse fMRI analysis using 3D U-Net

2021 ◽  
Author(s):  
Guohui Ruan ◽  
Jiaming Liu ◽  
Ziqi An ◽  
Kaiibin Wu ◽  
Chuanjun Tong ◽  
...  
Keyword(s):  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Resting State Fmri ◽  
Jaccard Index ◽  
Brain Extraction ◽  
Functional Images ◽  
Skull Stripping ◽  
Anatomical Images ◽  
Fmri Analysis ◽  
T2 Weighted Images

Skull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are identical to those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.

scholarly journals 3D U-Net for Skull Stripping in Brain MRI

2019 ◽  
Vol 9 (3) ◽  
pp. 569 ◽  
Author(s):  
Hyunho Hwang ◽  
Hafiz Zia Ur Rehman ◽  
Sungon Lee
Keyword(s):  
Deep Learning ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Segmentation Algorithms ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Learning Network ◽  
The Brain

Skull stripping in brain magnetic resonance imaging (MRI) is an essential step to analyze images of the brain. Although manual segmentation has the highest accuracy, it is a time-consuming task. Therefore, various automatic segmentation algorithms of the brain in MRI have been devised and proposed previously. However, there is still no method that solves the entire brain extraction problem satisfactorily for diverse datasets in a generic and robust way. To address these shortcomings of existing methods, we propose the use of a 3D-UNet for skull stripping in brain MRI. The 3D-UNet was recently proposed and has been widely used for volumetric segmentation in medical images due to its outstanding performance. It is an extended version of the previously proposed 2D-UNet, which is based on a deep learning network, specifically, the convolutional neural network. We evaluated 3D-UNet skull-stripping using a publicly available brain MRI dataset and compared the results with three existing methods (BSE, ROBEX, and Kleesiek’s method; BSE and ROBEX are two conventional methods, and Kleesiek’s method is based on deep learning). The 3D-UNet outperforms two typical methods and shows comparable results with the specific deep learning-based algorithm, exhibiting a mean Dice coefficient of 0.9903, a sensitivity of 0.9853, and a specificity of 0.9953.

scholarly journals The Preprocessed Connectomes Project Repository of Manually Corrected Skull-stripped T1-weighted Anatomical MRI Data

2016 ◽  
Author(s):  
Benjamin Puccio ◽  
James P Pooley ◽  
John S Pellman ◽  
Elise C Taverna ◽  
R Cameron Craddock
Keyword(s):  
Standard Data ◽  
Brain Extraction ◽  
Skull Stripping ◽  
Testing Data ◽  
Anatomical Images ◽  
Automatic Methods ◽  
Non Local ◽  
Anatomical Mri ◽  
The Impact

AbstractBackgroundSkull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure is necessary for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually-corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study.FindingsSkull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on non local segmentation technique (BEaST) software and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was reiterated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data.ConclusionSkull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve their preprocessing of the Neurofeedback data, and as training and testing data for developing new skull-stripping algorithms and evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.

scholarly journals Automatic Segmentation and Measurement of Vascular Biomarkers in OCT-A Images

Proceedings ◽  
2018 ◽  
Vol 2 (18) ◽  
pp. 1169
Author(s):  
Macarena Díaz ◽  
Jorge Novo ◽  
Manuel G. Penedo ◽  
Marcos Ortega
Keyword(s):  
Correlation Coefficient ◽  
Automatic Segmentation ◽  
Clinical Analysis ◽  
Jaccard Index ◽  
Monitoring Process ◽  
Vascular Biomarkers

We propose an automatic methodology that identifies the vascularity zones in OCT-A images and their measurement for its use in clinical analysis and diagnostic processes. The segmentation and measurement contributes objectivity and repeatability in the results, desirable characteristics in any diagnosis and monitoring process. In the validation of the method, the correlation coefficient of Pearson and Jaccard index were used, obtaining satisfactory results.

scholarly journals Paraspinal Muscle Segmentation Based on Deep Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2650 ◽  
Author(s):  
Haixing Li ◽  
Haibo Luo ◽  
Yunpeng Liu
Keyword(s):  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Automatic Measurement ◽  
Automated Analysis ◽  
Erector Spinae ◽  
Paraspinal Muscle ◽  
Spinal Diseases ◽  
Dice Coefficient ◽  
Feature Maps ◽  
Paravertebral Muscles

The accurate segmentation of the paraspinal muscle in Magnetic Resonance (MR) images is a critical step in the automated analysis of lumbar diseases such as chronic low back pain, disc herniation and lumbar spinal stenosis. However, the automatic segmentation of multifidus and erector spinae has not yet been achieved due to three unusual challenges: (1) the muscle boundary is unclear; (2) the gray histogram distribution of the target overlaps with the background; (3) the intra- and inter-patient shape is variable. We propose to tackle the problem of the automatic segmentation of paravertebral muscles using a deformed U-net consisting of two main modules: the residual module and the feature pyramid attention (FPA) module. The residual module can directly return the gradient while preserving the details of the image to make the model easier to train. The FPA module fuses different scales of context information and provides useful salient features for high-level feature maps. In this paper, 120 cases were used for experiments, which were provided and labeled by the spine surgery department of Shengjing Hospital of China Medical University. The experimental results show that the model can achieve higher predictive capability. The dice coefficient of the multifidus is as high as 0.949, and the Hausdorff distance is 4.62 mm. The dice coefficient of the erector spinae is 0.913 and the Hausdorff distance is 7.89 mm. The work of this paper will contribute to the development of an automatic measurement system for paraspinal muscles, which is of great significance for the treatment of spinal diseases.

T131. KNCQ Channel Opener Ezogabine_x000B_as a Treatment for Depression: A Preliminary Resting State fMRI Analysis

2018 ◽  
Vol 83 (9) ◽  
pp. S179
Author(s):  
Aaron Tan ◽  
Sara Costi ◽  
Laurel Morris ◽  
Nicholas Van Dam ◽  
James Murrough
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Channel Opener ◽  
Fmri Analysis

scholarly journals Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

2020 ◽  
Author(s):  
Geliang Wang ◽  
Yajie Hu ◽  
Xianjun Li ◽  
Miaomiao Wang ◽  
Congcong Liu ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Full Term ◽  
Neonatal Brain ◽  
Tissue Segmentation ◽  
Term Neonates ◽  
Brain Extraction ◽  
Brain Tissue Segmentation ◽  
Skull Stripping ◽  
Structural Network ◽  
Manual Correction

Abstract Background: Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about how accuracy of the skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FSL’s Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network. Methods: Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a John Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, C p ; characteristic path length, L p ; local efficiency, E local ; global efficiency, E global ) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volumes between three workflows. Results: There were significant differences in volumes of 48 brain region between three workflows ( P <0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased C p , increased L p , decreased E local , and E global , in contrast to two automatic ones. Conclusions: Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

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