google ads
Search
CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning CTisus CT Scanning Ask the Fish

Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Deep Learning and Brain Apps Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Deep Learning and Brain Apps

-- OR --

  • Brain Tumors: Pathology
    We here demonstrate that DNA methylation-based CNS tumour classification using a comprehensive machine-learning approach is a valuable asset for clinical decision-making. In particular, the high level of standardization has great promise to reduce the substantial inter-observer variability observed in current CNS tumour diagnostics.
  • “We here demonstrate that DNA methylation-based CNS tumour classification using a comprehensive machine-learning approach is a valuable asset for clinical decision-making. In particular, the high level of standardization has great promise to reduce the substantial inter-observer variability observed in current CNS tumour diagnostics.”
    DNA methylation-based classification of central nervous system tumours
    Pfister SM et al.
    Nature 2018 (in press)
  • “We here demonstrate that DNA methylation-based CNS tumour classification using a comprehensive machine-learning approach is a valuable asset for clinical decision-making. In particular, the high level of standardization has great promise to reduce the substantial inter-observer variability observed in current CNS tumour diagnostics. Furthermore, in contrast to traditional pathology, where there is a pressure to assign all tumours to a described entity even for atypical or challenging cases, the objective measure that we provide here allows for ‘no match’ to a defined class.”
    DNA methylation-based classification of central nervous system tumours
    Nature 2018 (in press)
  • Purpose: To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers.
    Materials and Methods: Prospective 18F-FDG PET brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (2109 imaging studies from 2005 to 2017, 1002 patients) and retrospective independent test set (40 imaging studies from 2006 to 2016, 40 patients) were collected. Final clinical diagnosis at follow-up was recorded. Convolutional neural network of InceptionV3 architecture was trained on 90% of ADNI data set and tested on the remaining 10%, as well as the independent test set, with performance compared to radiologic readers. Model was analyzed with sensitivity, specificity, receiver operating characteristic (ROC), saliency map, and t-distributed stochastic neighbor embedding.
    Results: The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00) when evaluated on predicting the final clinical diagnosis of AD in the independent test set (82% specificity at 100% sensitivity), an average of 75.8 months prior to the final diagnosis, which in ROC space outperformed reader performance (57% [four of seven] sensitivity, 91% [30 of 33] specificity; P , .05). Saliency map demonstrated attention to known areas of interest but with focus on the entire brain.
    Conclusion: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9 
    https://doi.org/10.1148/radiol.2018180958
  • In this study, we aimed to evaluate whether a deep learning algorithm could be trained to predict the final clinical diagnoses in patients who underwent 18F-FDG PET of the brain and, once trained, how the deep learning algorithm compares with the cur- rent standard clinical reading methods in differentiation of patients with final diagnoses of AD, MCI, or no evidence of dementia. We hypothesized that the deep learning algorithm could detect features or patterns that are not evident on standard clinical review of images and thereby improve the final diagnostic classification of individuals.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/radiol.2018180958

  • A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/iol.2018180958
  • Our study had several limitations. First, our independent test data were relatively small (n = 40) and were not collected as part of a clinical trial. Most notably, this was a highly selected cohort in that all patients must have been referred to the memory clinic and neurologist must have decided that a PET study of the brain would be useful in clinical management. This effectively excluded most non-AD neurodegenerative cases and other neurologic disorders such as stroke that could affect memory function. Arguably, such cohort of patients would be the most relevant group to test the deep learning algorithm, but the algorithm’s performance on a more general patient population remains untested and un- proven, hence the pilot nature of this study.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/iol.201818095
  • Purpose: To develop and validate a deep learning algorithm that predicts the final diagnosis of Alzheimer disease (AD), mild cognitive impairment, or neither at fluorine 18 (18F) fluorodeoxyglucose (FDG) PET of the brain and compare its performance to that of radiologic readers.
    Conclusion: By using fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning algorithm developed for early prediction of Alzheimer disease achieved 82% specificity at 100% sensitivity, an average of 75.8 months prior to the final diagnosis.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/radiol.2018180958
  • Second, the deep learning algorithm’s robustness is inherently limited by the clinical distribution of the training set from ADNI. The algorithm achieved strong performance on a small independent test set, where the population substantially differed from the ADNI test set; however, its performance and robustness cannot yet be guaranteed on prospective, unselected, and real-life scenario patient cohorts. Further validation with larger and prospective external test set must be performed before actual clinical use. Further- more, this training set from ADNI did not include non-AD neurodegenerative cases, limiting the utility of the algorithm in such patient population.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/iol.201818095
  • Third, the deep learning algorithm did not yield a human interpretable imaging biomarker despite visualization with saliency map, which highlights the inherent black-box limitation of deep learning algorithms. The algorithm instead made predictions based on holistic features of the imaging study, distinct from the human expert approaches. Fourth, MCI and non-AD/MCI were inherently unstable diagnoses in that their accuracy is dependent on the length of follow-up. For example, some of the MCI patients, if followed up for long enough time, may have eventually progressed to AD.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/iol.201818095
  • Overall, our study demonstrates that a deep learning algorithm can predict the final diagnosis of AD from 18F- FDG PET imaging studies of the brain with high accuracy and robustness across external test data. Furthermore, this study proposes a working deep learning approaches and a set of convolutional neural network hyperparameters, validated on a public dataset, that can be the groundwork for further model improvement. With further large-scale external validation on multi-institutional data and model calibration, the algorithm may be integrated into clinical workflow and serve as an important decision support tool to aid radiology readers and clinicians with early prediction of AD from 18F- FDG PET imaging studies.
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/iol.201818095
  • Overall, our study demonstrates that a deep learning algorithm can predict the final diagnosis of AD from 18F- FDG PET imaging studies of the brain with high accuracy and robustness across external test data. Furthermore, this study proposes a working deep learning approaches and a set of convolutional neural network hyperparameters, validated on a public dataset, that can be the groundwork for further model improvement.”
    A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain
    Yiming Ding et al.
    Radiology 2018; 00:1–9
    https://doi.org/10.1148/iol.201818095
  • High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3Dconvolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features. Along with the pivotal clinical features, we finally train a support vector machine to predict if the patient has a long or short overall survival (OS) time. Experimental results demonstrate that our methods can achieve an accuracy as high as 89.9% We also find that the learned features from fMRI and DTI play more important roles in accurately predicting the OS time, which provides valuable insights into functional neuro-oncological applications.

    
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.
Nie D et al.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:212-222
  • “High-grade glioma is the most aggressive and severe brain tumor that leads to death of almost 50% patients in 1-2 years. Thus, accurate prognosis for glioma patients would provide essential guidelines for their treatment planning. Conventional survival prediction generally utilizes clinical information and limited handcrafted features from magnetic resonance images (MRI), which is often time consuming, laborious and subjective. In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients. Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features.”

    
3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.
Nie D et al.
Med Image Comput Comput Assist Interv. 2016 Oct;9901:212-220
  • “While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease.”


    Classification of CT brain images based on deep learning networks.
Gao XW1, Hui R2, Tian Z
Comput Methods Programs Biomed. 2017 Jan;138:49-56
© 1999-2018 Elliot K. Fishman, MD, FACR. All rights reserved.