Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications

Rabbani, Arash; Gao, Hao; Lazarus, Alan; Dalton, David R.; Ge, Yuzhang; Mangion, Kenneth; Berry, Colin; Husmeier, Dirk

doi:10.1016/j.compmedimag.2023.102203

Cited by 6 publications

(5 citation statements)

References 63 publications

(99 reference statements)

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“…The second challenge is to accurately extract the time series of LVV from MRI scans [10]. In our experiments, all LVVs were synthetically generated based on simulations from the cardiac model, which guarantees a higher degree of accuracy than might be available in real clinical applications.…”

Section: Discussionmentioning

confidence: 99%

“…Our present work focuses on only one of the QoIs considered in [4] -the left ventricular volume (LVV) at end-diastole -and takes a time series of different measurements between early and end-diastole, instead of a single time point. Focusing on only the LVV is motivated with clinical applications in mind, as this quantity can be extracted from MRI scans automatically using recent advances in machine learning [10]. Our approach is naturally formulated as an emulator of a scalar quantity (the LVV) defined over a Cartesian product space of biophysical parameters X and time T.…”

Section: Time-series Gaussian Processmentioning

confidence: 99%

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Bayesian inference of cardiac models emulated with a time series Gaussian process

Ge,

Husmeier,

Lazarus

et al. 2023

Proceedings of the 5th International Conference on Statistics: Theory and Applications

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The objective of this research is to estimate the specific biophysical parameters of a cardiac mechanics model using a time series of variables that can be acquired in the clinic. This method is driven by the need to infer the passive stiffness of the myocardium to diagnose cardiophysiological diseases, which requires the measurement of the volume of the left ventricle (LV) of the heart at different time points. Although there have been many advancements in cardiac models, their computation is complex and costly. To overcome this challenge, we propose a method that utilizes a Gaussian process to construct a statistical model for emulation. Since the LV volumes are acquired in a time series, we employ the Kronecker product to compute two covariance matrices separately for time and biophysical parameters. Once we construct an accurate emulator to represent the passive filling process of the cardiac mechanics model during diastole, we can estimate the biophysical parameters inversely. We also aim to evaluate the impact of increasing the number of time points on reducing the uncertainty of the inverse estimation in this study.

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Section: Discussionmentioning

confidence: 99%

Section: Time-series Gaussian Processmentioning

confidence: 99%

Bayesian inference of cardiac models emulated with a time series Gaussian process

Ge,

Husmeier,

Lazarus

et al. 2023

Proceedings of the 5th International Conference on Statistics: Theory and Applications

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show abstract

“…The average of the squared error is called the Mean Squared Error (MSE) [44]. MSE reveals how close a regression line is to a set of points, which is done by taking the distances from the points to the regression line.…”

Section: ) Mean Squared Error (Mse)mentioning

confidence: 99%

“…7) Root Mean Squared Error (RMSE): Root Mean Squared Error (RMSE) is a recognized way to find the error of a regression model, which defines how close a fitted line is to data points [44]. RMSE was estimated using Eq.…”

Section: ) Root Mean Squared Logarithmic Error (Rmsle)mentioning

confidence: 99%

Computer Vision-based Efficient Segmentation Method for Left Ventricular Epicardium and Endocardium using Deep Learning

Saif,

Duong,

Holden

2023

IJACSA

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Segmentation of the Left Ventricular Epicardium and Endocardium remains challenging and significant for valuable investigation of cardiac image classification. Previous research methods did not consider the flexibility of the heart area, so measurements needed to be more consistent and accurate. In addition, previous methods ignored the presence of affectability and additional parts, such as the lung organ inside the frame, during segmentation. Deep learning architectures, specifically convolutional neural networks, have become the primary choice for assessing cardiac medical images. In this context, a Convolutional Neural Network (CNN) can be an effective way to segment the left ventricular epicardium and endocardium as CNN can take data pictures, move enormity to various centers or objects in the image and have the choice to separate one from the other. This research proposes an efficient method for segmenting the left ventricular epicardium and endocardium using the InceptionV3 convolutional neural network. Rather than including fully connected layers on the head of the component maps, the proposed method considers the average of each element map, and the subsequent vector was taken care of legitimately into the SoftMax layer. Data augmentation technique was used to validate the proposed method on large number of dataset images. Besides, the proposed method was validated in publicly available MRI cardiac image datasets. Comprehensive experimental analysis was done by analyzing a large number of performance metrics, i.e., cosine similarity, log cos error, mean absolute error, mean absolute percentage error, mean squared error, mean squared logarithmic error, and root mean squared error. The proposed method depicted superior performance for localization of the left ventricular epicardium and endocardium in terms of all these performance metrics. In addition, the proposed method performed efficiently to get smooth curve for covering the region due to usage of interpolation technique to draw the curve, which made it smoother compared with previous research.

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“…Wang et al [7] conducted a study on deep learning-based visual detection of marine organisms to improve the accuracy of detection and identification of marine organisms based on previous studies, and this study is significantly helpful for the conservation of marine organisms and ecology. Rabbani et al [8] applied deep learning to accurately estimate the left ventricular chamber volume and controlled the volume error to 8 ml, and these data can be further used for treatment planning and diagnosis of patients with cardiovascular diseases. Lin et al [9] evaluated the safe construction of underground tunnels based on a deep learning approach, and the system provides a reference for achieving the safe construction of underground tunnels.…”

Section: Introductionmentioning

confidence: 99%

Web-based automatic picture object recognition system

Wang,

Pan

2023

Fourth International Conference on Signal Processing and Computer Science (SPCS 2023)

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Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications

Cited by 6 publications

References 63 publications

Bayesian inference of cardiac models emulated with a time series Gaussian process

Bayesian inference of cardiac models emulated with a time series Gaussian process

Computer Vision-based Efficient Segmentation Method for Left Ventricular Epicardium and Endocardium using Deep Learning

Web-based automatic picture object recognition system

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