scholarly journals Automated claustrum segmentation in human brain MRI using deep learning

2021 ◽  
Author(s):  
Hongwei Li ◽  
Aurore Menegaux ◽  
Benita Schmitz‐Koep ◽  
Antonia Neubauer ◽  
Felix J. B. Bäuerlein ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Mri

scholarly journals DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI

2021 ◽  
Author(s):  
Vladimir Fonov ◽  
Mahsa Dadar ◽  
D. Louis Collins ◽  
◽  
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Deep Learning ◽  
Human Brain ◽  
Brain Mri ◽  
True Negative ◽  
Available N ◽  
True Negative Rate ◽  
Negative Rate ◽  
Mri Scans

Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.

scholarly journals Deep learning of quality control for stereotaxic registration of human brain MRI

2018 ◽  
Author(s):  
Vladimir S. Fonov ◽  
Mahsa Dadar ◽  
D. Louis Collins ◽  
Keyword(s):  
Image Processing ◽  
Quality Control ◽  
Deep Learning ◽  
Human Brain ◽  
Control Method ◽  
False Positive Rate ◽  
Brain Mri ◽  
Brain Scans ◽  
Positive Rate ◽  
Mri Scans

AbstractLinear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the following image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none of them has a 100% success rate. Manual assessment of the registration is commonly used as part of quality control.We propose a completely automatic quality control method based on deep learning that replaces human rater and accurately performs quality control assessment for stereotaxic registration of T1w brain scans.In a recently published study from our group comparing linear registration methods, we used a database of 9693 MRI scans from several publically available datasets and applied five linear registration tools. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed.Our method was able to achieve 88% accuracy and 11% false positive rate in detecting scans that should pass quality control, better than a manual QC rater.

scholarly journals Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

2019 ◽  
Vol 9 (22) ◽  
pp. 4749
Author(s):  
Lingyun Jiang ◽  
Kai Qiao ◽  
Linyuan Wang ◽  
Chi Zhang ◽  
Jian Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Activity ◽  
Feature Space ◽  
Training Data ◽  
Reconstruction Method ◽  
Learning Method ◽  
Training Samples ◽  
Visual Reconstruction ◽  
Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

Brain MRI analysis using a deep learning based evolutionary approach

2020 ◽  
Vol 126 ◽  
pp. 218-234 ◽  
Author(s):  
Hossein Shahamat ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Evolutionary Approach

Deep learning–based methods may minimize GBCA dosage in brain MRI

2021 ◽  
Author(s):  
Huanyu Luo ◽  
Tao Zhang ◽  
Nan-Jie Gong ◽  
Jonthan Tamir ◽  
Srivathsa Pasumarthi Venkata ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Mri

scholarly journals Deep learning from MRI-derived labels enables automatic brain tissue classification on human brain CT

NeuroImage ◽  
2021 ◽  
pp. 118606
Author(s):  
Meera Srikrishna ◽  
Joana B. Pereira ◽  
Rolf A. Heckemann ◽  
Giovanni Volpe ◽  
Danielle van Westen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Human Brain ◽  
Brain Tissue ◽  
Tissue Classification ◽  
Brain Ct

scholarly journals Highest Resolution In Vivo Human Brain MRI Using Prospective Motion Correction

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0133921 ◽  
Author(s):  
Daniel Stucht ◽  
K. Appu Danishad ◽  
Peter Schulze ◽  
Frank Godenschweger ◽  
Maxim Zaitsev ◽  
...  
Keyword(s):  
Human Brain ◽  
Motion Correction ◽  
Brain Mri

Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net

2021 ◽  
Author(s):  
Shidong Li ◽  
Jianwei Liu ◽  
Zhanjie Song
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
The Impact

Abstract Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-ofinterest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

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