scholarly journals A Deep Learning Based Cardiac Cine Segmentation Framework for Clinicians – Transfer Learning Application to 7T

2020 ◽  
Author(s):  
Markus Johannes Ankenbrand ◽  
David Lohr ◽  
Wiebke Schlötelburg ◽  
Theresa Reiter ◽  
Tobias Wech ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Left Ventricular ◽  
Cine Image ◽  
Cine Mri ◽  
Data Set ◽  
Cardiac Cine ◽  
Starting Point ◽  
Segmentation Task ◽  
Data Requirements

AbstractBackgroundArtificial neural networks have shown promising performance in automatic segmentation of cardiac magnetic resonance imaging. However, initial training of such networks requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is often limited. Transfer learning has been proposed to address this challenge, but specific recommendations on the type and amount of data required is lacking. In this study we aim to assess data requirements for transfer learning to cardiac 7T in humans where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches of other researchers and clinicians.MethodsA publicly available model for bi-ventricular segmentation is used to annotate a publicly available data set. This labelled data set is subsequently used to train a neural network for segmentation of left ventricular and myocardial contours in cardiac cine MRI. The network is used as starting point for transfer learning to the segmentation task on 7T cine data of healthy volunteers (n=22, 7873 images). Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning.ResultsInconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the initial model achieved DICELV=0.835 and DICEMY=0.670. Transfer learning using 7T cine data and ImageNet weight initialization significantly (p<10−3) improved model performance to DICELV=0.900 and DICEMY=0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICELV=0.908, DICEMY=0.805).ConclusionsThis work demonstrates the benefits of transfer learning for cardiac cine image segmentation on a quantitative basis. We also make data, models and code publicly available, while providing practical guidelines for researchers planning transfer learning projects in cardiac MRI.

scholarly journals Ambulatory and Laboratory Stress Detection Based on Raw Electrocardiogram Signals Using a Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4408 ◽  
Author(s):  
Hyun-Myung Cho ◽  
Heesu Park ◽  
Suh-Yeon Dong ◽  
Inchan Youn
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Mental Arithmetic ◽  
Small Data ◽  
Data Sets ◽  
Learning Method ◽  
Data Set ◽  
Electrocardiogram Signals ◽  
Proposed Model ◽  
Small Data Set

The goals of this study are the suggestion of a better classification method for detecting stressed states based on raw electrocardiogram (ECG) data and a method for training a deep neural network (DNN) with a smaller data set. We suggest an end-to-end architecture to detect stress using raw ECGs. The architecture consists of successive stages that contain convolutional layers. In this study, two kinds of data sets are used to train and validate the model: A driving data set and a mental arithmetic data set, which smaller than the driving data set. We apply a transfer learning method to train a model with a small data set. The proposed model shows better performance, based on receiver operating curves, than conventional methods. Compared with other DNN methods using raw ECGs, the proposed model improves the accuracy from 87.39% to 90.19%. The transfer learning method improves accuracy by 12.01% and 10.06% when 10 s and 60 s of ECG signals, respectively, are used in the model. In conclusion, our model outperforms previous models using raw ECGs from a small data set and, so, we believe that our model can significantly contribute to mobile healthcare for stress management in daily life.

scholarly journals Is One Teacher Model Enough to Transfer Knowledge to a Student Model?

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 334
Author(s):  
Nicola Landro ◽  
Ignazio Gallo ◽  
Riccardo La Grassa
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Learning Problem ◽  
Learning Techniques ◽  
Starting Point ◽  
Learning Technique ◽  
Interesting Approach ◽  
Classification Tasks ◽  
Learned Features ◽  
Fine Tune

Nowadays, the transfer learning technique can be successfully applied in the deep learning field through techniques that fine-tune the CNN’s starting point so it may learn over a huge dataset such as ImageNet and continue to learn on a fixed dataset to achieve better performance. In this paper, we designed a transfer learning methodology that combines the learned features of different teachers to a student network in an end-to-end model, improving the performance of the student network in classification tasks over different datasets. In addition to this, we tried to answer the following questions which are in any case directly related to the transfer learning problem addressed here. Is it possible to improve the performance of a small neural network by using the knowledge gained from a more powerful neural network? Can a deep neural network outperform the teacher using transfer learning? Experimental results suggest that neural networks can transfer their learning to student networks using our proposed architecture, designed to bring to light a new interesting approach for transfer learning techniques. Finally, we provide details of the code and the experimental settings.

Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

2020 ◽  
Vol 83 (6) ◽  
pp. 602-614
Author(s):  
Hidir Selcuk Nogay ◽  
Hojjat Adeli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Epileptic Seizure ◽  
Binary Classification ◽  
Classification Model ◽  
Automatic Identification ◽  
Eeg Signal ◽  
Data Set

<b><i>Introduction:</i></b> The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. <b><i>Methods:</i></b> In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. <b><i>Results:</i></b> The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. <b><i>Discussion/Conclusion:</i></b> The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

scholarly journals Geometrical models for cardiac MRI in rodents: comparison of quantification of left ventricular volumes and function by various geometrical models with a full-volume MRI data set in rodents

2012 ◽  
Vol 302 (3) ◽  
pp. H709-H715 ◽  
Author(s):  
Tineke van de Weijer ◽  
Petronella A. van Ewijk ◽  
H. Reinier Zandbergen ◽  
Jos M. Slenter ◽  
Alfons G. Kessels ◽  
...  
Keyword(s):  
Cardiac Function ◽  
Cardiac Mri ◽  
Gold Standard ◽  
Left Ventricular ◽  
Cine Mri ◽  
Data Set ◽  
Single Plane ◽  
Lv Volumes ◽  
And Function ◽  
Full Volume

MRI has been proven to be an accurate method for noninvasive assessment of cardiac function. One of the current limitations of cardiac MRI is that it is time consuming. Therefore, various geometrical models are used, which can reduce scan and postprocessing time. It is unclear how appropriate their use is in rodents. Left ventricular (LV) volumes and ejection fraction (EF) were quantified based on 7.0 Tesla cine-MRI in 12 wild-type (WT) mice, 12 adipose triglyceride lipase knockout (ATGL−/−) mice (model of impaired cardiac function), and 11 rats in which we induced cardiac ischemia. The LV volumes and function were either assessed with parallel short-axis slices covering the full volume of the left ventricle (FV, gold standard) or with various geometrical models [modified Simpson rule (SR), biplane ellipsoid (BP), hemisphere cylinder (HC), single-plane ellipsoid (SP), and modified Teichholz Formula (TF)]. Reproducibility of the different models was tested and results were correlated with the gold standard (FV). All models and the FV data set provided reproducible results for the LV volumes and EF, with interclass correlation coefficients ≥0.87. All models significantly over- or underestimated EF, except for SR. Good correlation was found for all volumes and EF for the SR model compared with the FV data set ( R2 ranged between 0.59–0.95 for all parameters). The HC model and BP model also predicted EF well ( R2 ≥ 0.85), although proved to be less useful for quantitative analysis. The SP and TF models correlated poorly with the FV data set ( R2 ≥ 0.45 for EF and R2 ≥ 0.29 for EF, respectively). For the reduction in acquisition and postprocessing time, only the SR model proved to be a valuable method for calculating LV volumes, stroke volume, and EF.

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.

Sign in / Sign up

Export Citation Format

Share Document