scholarly journals Fully Automatic Atrial Fibrosis Assessment Using a Multilabel Convolutional Neural Network

2020 ◽  
Vol 13 (12) ◽  
Author(s):  
Orod Razeghi ◽  
Iain Sim ◽  
Caroline H. Roney ◽  
Rashed Karim ◽  
Henry Chubb ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Intensity Ratio ◽  
Blood Pool ◽  
Atrial Fibrosis ◽  
Data Set ◽  
Image Intensity ◽  
Fully Automatic

Background: Pathological atrial fibrosis is a major contributor to sustained atrial fibrillation. Currently, late gadolinium enhancement (LGE) scans provide the only noninvasive estimate of atrial fibrosis. However, widespread adoption of atrial LGE has been hindered partly by nonstandardized image processing techniques, which can be operator and algorithm dependent. Minimal validation and limited access to transparent software platforms have also exacerbated the problem. This study aims to estimate atrial fibrosis from cardiac magnetic resonance scans using a reproducible operator-independent fully automatic open-source end-to-end pipeline. Methods: A multilabel convolutional neural network was designed to accurately delineate atrial structures including the blood pool, pulmonary veins, and mitral valve. The output from the network removed the operator dependent steps in a reproducible pipeline and allowed for automated estimation of atrial fibrosis from LGE-cardiac magnetic resonance scans. The pipeline results were compared against manual fibrosis burdens, calculated using published thresholds: image intensity ratio 0.97, image intensity ratio 1.61, and mean blood pool signal +3.3 SD. Results: We validated our methods on a large 3-dimensional LGE-cardiac magnetic resonance data set from 207 labeled scans. Automatic atrial segmentation achieved a 91% Dice score, compared with the mutual agreement of 85% in Dice seen in the interobserver analysis of operators. Intraclass correlation coefficients of the automatic pipeline with manually generated results were excellent and better than or equal to interobserver correlations for all 3 thresholds: 0.94 versus 0.88, 0.99 versus 0.99, 0.99 versus 0.96 for image intensity ratio 0.97, image intensity ratio 1.61, and +3.3 SD thresholds, respectively. Automatic analysis required 3 minutes per case on a standard workstation. The network and the analysis software are publicly available. Conclusions: Our pipeline provides a fully automatic estimation of fibrosis burden from LGE-cardiac magnetic resonance scans that is comparable to manual analysis. This removes one key source of variability in the measurement of atrial fibrosis.

scholarly journals Convolutional Neural Network for the Detection of End-Diastole and End-Systole Frames in Free-Breathing Cardiac Magnetic Resonance Imaging

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Fan Yang ◽  
Yan He ◽  
Mubashir Hussain ◽  
Hong Xie ◽  
Pinggui Lei
Keyword(s):  
Neural Network ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Free Breathing ◽  
Automatic Identification ◽  
Motion Signal ◽  
Examination Time ◽  
High Degree ◽  
Cmr Imaging

Free-breathing cardiac magnetic resonance (CMR) imaging has short examination time with high reproducibility. Detection of the end-diastole and the end-systole frames of the free-breathing cardiac magnetic resonance, supplemented by visual identification, is time consuming and laborious. We propose a novel method for automatic identification of both the end-diastole and the end-systole frames, in the free-breathing CMR imaging. The proposed technique utilizes the convolutional neural network to locate the left ventricle and to obtain the end-diastole and the end-systole frames from the respiratory motion signal. The proposed procedure works successfully on our free-breathing CMR data, and the results demonstrate a high degree of accuracy and stability. Convolutional neural network improves the postprocessing efficiency greatly and facilitates the clinical application of the free-breathing CMR imaging.

scholarly journals Magnetic resonance image intensity ratio, a normalized measure to enable interpatient comparability of left atrial fibrosis

Heart Rhythm ◽  
2014 ◽  
Vol 11 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Irfan M. Khurram ◽  
Roy Beinart ◽  
Vadim Zipunnikov ◽  
Jane Dewire ◽  
Hirad Yarmohammadi ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Intensity Ratio ◽  
Magnetic Resonance Image ◽  
Left Atrial ◽  
Atrial Fibrosis ◽  
Image Intensity ◽  
Left Atrial Fibrosis

scholarly journals A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3693
Author(s):  
Xuchu Wang ◽  
Fusheng Wang ◽  
Yanmin Niu
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Dictionary Learning ◽  
Heart Diseases ◽  
Volume Measurement ◽  
Blood Pool ◽  
Left Ventricular ◽  
Data Set ◽  
Discriminative Dictionary Learning

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

scholarly journals Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fanar E. K. Al-Khuzaie ◽  
Oguz Bayat ◽  
Adil D. Duru
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Magnetic Resonance ◽  
Convolutional Neural Network ◽  
Dropout Rate ◽  
Resonance Imaging ◽  
Data Set ◽  
The Brain

There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer’s patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer’s patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet.

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