Diagnosis of left ventricular hypertrophy using convolutional neural network

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results.Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the measurement method proposed in this paper has the advantages of less manual intervention, and the processing method is reasonable and has practical value.

Download Full-text

Diagnosis of left ventricular hypertrophybased on convolutional neural network

10.21203/rs.2.19295/v1 ◽

2019 ◽

Author(s):

Zini Jian ◽

Xianpei Wang ◽

Jingzhe Zhang ◽

Xinyu Wang ◽

Youbin Deng

Keyword(s):

Neural Network ◽

Left Ventricle ◽

Convolutional Neural Network ◽

Posterior Wall ◽

Video Stream ◽

Detection Algorithm ◽

Left Ventricular ◽

Diagnostic Significance ◽

Design Algorithm ◽

Actual Measurement

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, which is not only time-consuming and laborious, but also difficult to accurately locate the edge, which will bring errors to the measurement results. Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the method proposed in this paper not only has the advantage of less manual intervention, but also can reduce the workload of doctors.

Download Full-text

Diagnosis of left ventricular hypertrophy using convolutional neural network

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-020-01255-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Zini Jian ◽

Xianpei Wang ◽

Jingzhe Zhang ◽

Xinyu Wang ◽

Youbin Deng

Keyword(s):

Neural Network ◽

Left Ventricle ◽

Convolutional Neural Network ◽

Posterior Wall ◽

Video Stream ◽

Detection Algorithm ◽

Left Ventricular ◽

Diagnostic Significance ◽

Design Algorithm ◽

Actual Measurement

Abstract Background Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor’s selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results. Methods In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions Therefore, the measurement method proposed in this paper has the advantages of less manual intervention, and the processing method is reasonable and has practical value.

Download Full-text

Diagnosis of left ventricular hypertrophybased on convolutional neural network

10.21203/rs.3.rs-18367/v1 ◽

2020 ◽

Author(s):

Zini Jian ◽

Xianpei Wang ◽

Jingzhe Zhang ◽

Xinyu Wang ◽

Youbin Deng

Keyword(s):

Neural Network ◽

Left Ventricle ◽

Convolutional Neural Network ◽

Posterior Wall ◽

Video Stream ◽

Detection Algorithm ◽

Left Ventricular ◽

Diagnostic Significance ◽

Design Algorithm ◽

Actual Measurement

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, which is not only time-consuming and laborious, but also difficult to accurately locate the edge, which will bring errors to the measurement results.Material/Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the method proposed in this paper not only has the advantage of less manual intervention, but also can reduce the workload of doctors.

Download Full-text

Diagnosis of left ventricular hypertrophy using convolutional neural network

10.21203/rs.3.rs-18367/v2 ◽

2020 ◽

Author(s):

Zini Jian ◽

Xianpei Wang ◽

Jingzhe Zhang ◽

Xinyu Wang ◽

Youbin Deng

Keyword(s):

Neural Network ◽

Left Ventricle ◽

Convolutional Neural Network ◽

Posterior Wall ◽

Video Stream ◽

Detection Algorithm ◽

Left Ventricular ◽

Diagnostic Significance ◽

Design Algorithm ◽

Actual Measurement

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results.Material/Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the method proposed in this paper not only has the advantage of less manual intervention, but also can reduce the workload of doctors.

Download Full-text

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

Sensors ◽

10.3390/s21113693 ◽

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.

Download Full-text

Multi-Scale Feature Fusion Convolutional Neural Network for Concurrent Segmentation of Left Ventricle and Myocardium in Cardiac MR Images

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3005 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1023-1032

Author(s):

Lin Qi ◽

Haoran Zhang ◽

Xuehao Cao ◽

Xuyang Lyu ◽

Lisheng Xu ◽

...

Keyword(s):

Neural Network ◽

Left Ventricle ◽

Convolutional Neural Network ◽

Feature Fusion ◽

Left Ventricular ◽

Mr Images ◽

Feature Maps ◽

Scale Feature ◽

Multi Scale ◽

Cine Mr

Accurate segmentation of the blood pool of left ventricle (LV) and myocardium (or left ventricular epicardium, MYO) from cardiac magnetic resonance (MR) can help doctors to quantify LV ejection fraction and myocardial deformation. To reduce doctor’s burden of manual segmentation, in this study, we propose an automated and concurrent segmentation method of the LV and MYO. First, we employ a convolutional neural network (CNN) architecture to extract the region of interest (ROI) from short-axis cardiac cine MR images as a preprocessing step. Next, we present a multi-scale feature fusion (MSFF) CNN with a new weighted Dice index (WDI) loss function to get the concurrent segmentation of the LV and MYO. We use MSFF modules with three scales to extract different features, and then concatenate feature maps by the short and long skip connections in the encoder and decoder path to capture more complete context information and geometry structure for better segmentation. Finally, we compare the proposed method with Fully Convolutional Networks (FCN) and U-Net on the combined cardiac datasets from MICCAI 2009 and ACDC 2017. Experimental results demonstrate that the proposed method could perform effectively on LV and MYOs segmentation in the combined datasets, indicating its potential for clinical application.

Download Full-text

A Detection algorithm based on Convolutional Neural Network

10.20944/preprints201811.0583.v1 ◽

2018 ◽

Author(s):

Fei Rong ◽

Li Shasha ◽

Xu Qingzheng ◽

Liu Kun

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Training Model ◽

Test Sample ◽

Training Sample ◽

Detection Algorithm ◽

Small Scale ◽

Detection Model ◽

Sample Data ◽

Ratio Change

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

Download Full-text

Loop Closure Detection Algorithm Based On Multi-Level Convolutional Neural Network Features

Laser & Optoelectronics Progress ◽

10.3788/lop55.111507 ◽

2018 ◽

Vol 55 (11) ◽

pp. 111507

Author(s):

鲍振强 Bao Zhenqiang ◽

李艾华 Li Aihua ◽

崔智高 Cui Zhigao ◽

苏延召 Su Yanzhao ◽

郑勇 Zheng Yong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Detection Algorithm ◽

Loop Closure ◽

Loop Closure Detection ◽

Multi Level

Download Full-text

3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽

10.1109/access.2019.2955995 ◽

2019 ◽

Vol 7 ◽

pp. 171461-171470

Author(s):

Dianwei Wang ◽

Yanhui He ◽

Ying Liu ◽

Daxiang Li ◽

Shiqian Wu ◽

...

Keyword(s):

Neural Network ◽

Object Detection ◽

Convolutional Neural Network ◽

Detection Algorithm ◽

3D Object ◽

Multi Scale ◽

Panoramic Images ◽

3D Object Detection

Download Full-text

P955 left ventricle cardiac remodeling among lithuanian football versus basketball players

European Heart Journal - Cardiovascular Imaging ◽

10.1093/ehjci/jez319.588 ◽

2020 ◽

Vol 21 (Supplement_1) ◽

Author(s):

A Aldujeli ◽

J Laukaitiene ◽

R Unikas

Keyword(s):

Left Ventricle ◽

Wall Thickness ◽

Cardiac Remodeling ◽

Interventricular Septum ◽

Posterior Wall ◽

Left Ventricular ◽

Relative Wall Thickness ◽

Football Players ◽

Basketball Players ◽

Lv Mass

Abstract Background Regular physical exercise causes a continuous gradual increase of the cardiac left ventricular (LV) mass known as physiological adaptive hypertrophy. The extent of LV remodeling depends on the type, amount, and intensity of the exercise. Purpose The aim of this study was to compare structural changes of the heart among Lithuanian football, basketball players and unathletic controls. Methods A total of 50 Lithuanian males aged between 20-29 years volunteered to participate in the study. Football players (n = 15) playing for local II league football clubs,and Basketball players (n = 15) playing for local minor league basketball teams. All athletes had been regularly engaged in their sport for at least three years. Inactive healthy volunteers (n = 20) of similar age served as controls. Routine transthoracic echocardiographic examinations to measure end-diastolic LV dimensions were performed by cardiology fellow under the supervision of a fully licensed cardiologist. Statistical analyses were performed using the SPSS 20.0 software. The value of p < 0,05 was considered as statistically significant. Results No structural or functional pathologies were evident during the echocardiographic examination in any of the subjects. Absolute interventricular septum (IVS) thickness and LV posterior wall thickness, but not LV diameter, were higher in athletes than in inactive controls (P < 0,001). Indexed LV diameter was higher in football players as compared with non-athlete controls and basketball players (P < 0,05). Left ventricular mass of all athletes were higher as compared with controls (p < 0.001). Relative wall thickness was not increased in football players but was higher in basketball players as compared with controls (p < 0.05). Conclusion Cardiac remodeling in Lithuanian football players resulted in left ventricle eccentric hypertrophy due to the LV dilation, increased LV mass and relatively normal relative wall thickness. However in Lithuanian basketball players we noticed an increase in both relative wall thickness and LV mass resulting in LV concentric hypertrophy. Echocardiographic characteristics Groups n End-diastolic LV diameter(mm) End-diastolic Interventricular septum (mm) End-diastolic LV posterior wall LV mass Football Players 15 56.9 10.8 10.8 242 Basketball players 15 53.6 11.5 11.3 254 Inactive individuals 20 53.2 9.1 9.5 182 P value 0.01 <0.001 <0.001 <0.01 Abstract P955 Figure.

Download Full-text