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 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

True Negative Rate

2011 ◽  
pp. 999-999
Author(s):  
William Uther ◽  
Dunja Mladenić ◽  
Massimiliano Ciaramita ◽  
Bettina Berendt ◽  
Aleksander Kołcz ◽  
...  
Keyword(s):  
True Negative ◽  
True Negative Rate ◽  
Negative Rate

Incorporation of relative cerebral blood flow into CT perfusion maps reduces false ’at risk' penumbra

2017 ◽  
Vol 10 (7) ◽  
pp. 657-662 ◽  
Author(s):  
Shlomi Peretz ◽  
David Orion ◽  
David Last ◽  
Yael Mardor ◽  
Yotam Kimmel ◽  
...  
Keyword(s):  
At Risk ◽  
Acute Stroke ◽  
Ct Perfusion ◽  
False Negative ◽  
Stroke Patients ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Perfusion Maps

PurposeThe region defined as ‘at risk’ penumbra by current CT perfusion (CTP) maps is largely overestimated. We aimed to quantitate the portion of true ‘at risk’ tissue within CTP penumbra and to determine the parameter and threshold that would optimally distinguish it from false ‘at risk’ tissue, that is, benign oligaemia.MethodsAmong acute stroke patients evaluated by multimodal CT (NCCT/CTA/CTP) we identified those that had not undergone endovascular/thrombolytic treatment and had follow-up NCCT. Maps of absolute and relative CBF, CBV, MTT, TTP and Tmax as well as summary maps depicting infarcted and penumbral regions were generated using the Intellispace Portal (Philips Healthcare, Best, Netherlands). Follow-up CT was automatically co-registered to the CTP scan and the final infarct region was manually outlined. Perfusion parameters were systematically analysed – the parameter that resulted in the highest true-negative-rate (ie, proportion of benign oligaemia correctly identified) at a fixed, clinically relevant false-negative-rate (ie, proportion of ‘missed’ infarct) of 15%, was chosen as optimal. It was then re-applied to the CTP data to produce corrected perfusion maps.ResultsForty seven acute stroke patients met selection criteria. Average portion of infarcted tissue within CTP penumbra was 15%±2.2%. Relative CBF at a threshold of 0.65 yielded the highest average true-negative-rate (48%), enabling reduction of the false ‘at risk’ penumbral region by ~half.ConclusionsApplying a relative CBF threshold on relative MTT-based CTP maps can significantly reduce false ‘at risk’ penumbra. This step may help to avoid unnecessary endovascular interventions.

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.

scholarly journals Assisting in Auditing of Buffer Overflow Vulnerabilities via Machine Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Qingkun Meng ◽  
Chao Feng ◽  
Bin Zhang ◽  
Chaojing Tang
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Buffer Overflow ◽  
Analysis Method ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Training Samples ◽  
Extended Code ◽  
Code Property

Buffer overflow vulnerability is a kind of consequence in which programmers’ intentions are not implemented correctly. In this paper, a static analysis method based on machine learning is proposed to assist in auditing buffer overflow vulnerabilities. First, an extended code property graph is constructed from the source code to extract seven kinds of static attributes, which are used to describe buffer properties. After embedding these attributes into a vector space, five frequently used machine learning algorithms are employed to classify the functions into suspicious vulnerable functions and secure ones. The five classifiers reached an average recall of 83.5%, average true negative rate of 85.9%, a best recall of 96.6%, and a best true negative rate of 91.4%. Due to the imbalance of the training samples, the average precision of the classifiers is 68.9% and the average F1 score is 75.2%. When the classifiers were applied to a new program, our method could reduce the false positive to 1/12 compared to Flawfinder.

scholarly journals genuMet: distinguish genuine untargeted metabolic features without quality control samples

2019 ◽  
Author(s):  
L Cao ◽  
C Clish ◽  
FB Hu ◽  
MA Martínez-González ◽  
C Razquin ◽  
...  
Keyword(s):  
Quality Control ◽  
Large Scale ◽  
Control Method ◽  
R Package ◽  
True Positive Rate ◽  
Supplementary Information ◽  
True Negative ◽  
True Negative Rate ◽  
Positive Rate ◽  
Measurement Artifacts

AbstractMotivationLarge-scale untargeted metabolomics experiments lead to detection of thousands of novel metabolic features as well as false positive artifacts. With the incorporation of pooled QC samples and corresponding bioinformatics algorithms, those measurement artifacts can be well quality controlled. However, it is impracticable for all the studies to apply such experimental design.ResultsWe introduce a post-alignment quality control method called genuMet, which is solely based on injection order of biological samples to identify potential false metabolic features. In terms of the missing pattern of metabolic signals, genuMet can reach over 95% true negative rate and 85% true positive rate with suitable parameters, compared with the algorithm utilizing pooled QC samples. genu-Met makes it possible for studies without pooled QC samples to reduce false metabolic signals and perform robust statistical analysis.Availability and implementationgenuMet is implemented in a R package and available on https://github.com/liucaomics/genuMet under GPL-v2 license.ContactLiming Liang: [email protected] informationSupplementary data are available at ….

Mastoid, middle ear and inner ear analysis in CT scan – a possible contribution for the identification of remains

2020 ◽  
Vol 60 (2) ◽  
pp. 102-111
Author(s):  
Henrique Rodrigues ◽  
Rosa Ramos ◽  
Leoni Fagundes ◽  
Orlando Galego ◽  
David Navega ◽  
...  
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Visual Assessment ◽  
True Positive Rate ◽  
Observer Agreement ◽  
True Positive ◽  
True Negative ◽  
True Negative Rate ◽  
Negative Rate ◽  
Positive Rate

Objective We aimed to evaluate whether the internal structures of the human ear have anatomical characteristics that are sufficiently distinctive to contribute to human identification and use in a forensic context. Materials and methods After data anonymisation, a dataset containing temporal bone CT scans of 100 subjects was processed by a radiologist who was not involved in the study. Four reference images were selected for each subject. Of the original sample, 10 examinations were used for visual comparison, case by case, against the dataset of 100 patients. This visual assessment was performed independently by four observers, who evaluated the anatomical agreement using a Likert scale (1–5). Inter-observer agreement, true positive rate, positive predictive value, true negative rate, negative predictive value, false positive rate, false negative rate and positive likelihood ratio (LR+) were evaluated. Results Inter-observer agreement obtained an overall Cohen’s Kappa = 99.59%. True positive rate, positive predictive value, true negative rate and negative predictive value were all 100%. Conclusion Visual assessment of the mastoid examinations was shown to be a robust and reliable approach to identify unique osseous features and contribute to human identification. The statistical analysis indicates that regardless of the examiner’s background and training, the approach has a high degree of accuracy.

True Negative Rate

2013 ◽  
pp. 3187-3187
Author(s):  
Daniel Campbell ◽  
Corey Ray-Subramanian ◽  
Winifred Schultz-Krohn ◽  
Kristen M. Powers ◽  
Renee Watling ◽  
...  
Keyword(s):  
True Negative ◽  
True Negative Rate ◽  
Negative Rate
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