“…3B). Notably, CFIN's ability to segment both cardiac chambers far surpassed those achieved when non-machine learning-based algorithms were applied to images of fluorescent zebrafish hearts at the same developmental stage (Krämer et al, 2009). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…While various traditional computational approaches exist to automate image segmentation (Sharma and Aggarwal, 2010), many of these methods still require some level of human input and can be limited in their ability to segment and classify distinct, yet similar, structures within biomedical images (Fei et al, 2016; Krämer et al, 2009; Packard et al, 2017). Several of these algorithms, including watershed segmentation, were previously evaluated for their ability to segment fluorescent images of embryonic zebrafish hearts at 48 hours post-fertilization (hpf) (Krämer et al, 2009). While these methods were capable of segmenting whole hearts from background, they failed to consistently delineate individual cardiac chambers.…”
Although the zebrafish embryo is a powerful animal model of human heart failure, the methods routinely employed to monitor cardiac function produce rough approximations that are susceptible to bias and inaccuracies. We developed and validated a deep learning-based image-analysis platform for automated extraction of volumetric parameters of cardiac function from dynamic light-sheet fluorescence microscopy (LSFM) images of embryonic zebrafish hearts. This platform, the Cardiac Functional Imaging Network (CFIN), automatically delivers rapid and accurate assessments of cardiac performance with greater sensitivity than current approaches.
“…3B). Notably, CFIN's ability to segment both cardiac chambers far surpassed those achieved when non-machine learning-based algorithms were applied to images of fluorescent zebrafish hearts at the same developmental stage (Krämer et al, 2009). Fig.…”
Section: Resultsmentioning
confidence: 99%
“…While various traditional computational approaches exist to automate image segmentation (Sharma and Aggarwal, 2010), many of these methods still require some level of human input and can be limited in their ability to segment and classify distinct, yet similar, structures within biomedical images (Fei et al, 2016; Krämer et al, 2009; Packard et al, 2017). Several of these algorithms, including watershed segmentation, were previously evaluated for their ability to segment fluorescent images of embryonic zebrafish hearts at 48 hours post-fertilization (hpf) (Krämer et al, 2009). While these methods were capable of segmenting whole hearts from background, they failed to consistently delineate individual cardiac chambers.…”
Although the zebrafish embryo is a powerful animal model of human heart failure, the methods routinely employed to monitor cardiac function produce rough approximations that are susceptible to bias and inaccuracies. We developed and validated a deep learning-based image-analysis platform for automated extraction of volumetric parameters of cardiac function from dynamic light-sheet fluorescence microscopy (LSFM) images of embryonic zebrafish hearts. This platform, the Cardiac Functional Imaging Network (CFIN), automatically delivers rapid and accurate assessments of cardiac performance with greater sensitivity than current approaches.
“…Manual segmentation of cardiac images in zebrafish and mice remains the gold standard for ground truth despite being a labor-intensive and error-prone method. Currently existing automatic methods, including adaptive binarization, clustering, voronoi-based segmentation, and watershed, have remained limited [24]. For instance, adaptive histogram thresholding provides a semi-automated computational approach to perform image segmentation; however, the output is detracted by variability in background-to-noise ratio from the standard image processing algorithms [23].…”
Objective: Recent advances in light-sheet fluorescence microscopy (LSFM) enable 3dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a challenge.
Methods:We hereby employed "subspace approximation with augmented kernels (Saak) transform" for accurate and efficient quantification of the light-sheet image stacks following chemotherapy-treatment. We established a machine learning framework with augmented kernels based on the Karhunen-Loeve Transform (KLT) to preserve linearity and reversibility of rectification.
Results:The Saak transform-based machine learning enhances computational efficiency and obviates iterative optimization of cost function needed for neural networks, minimizing the number of training datasets for segmentation in our scenario. The integration of forward and inverse Saak transforms can also serve as a light-weight module to filter adversarial perturbations and reconstruct estimated images, salvaging robustness of existing classification methods. The accuracy and robustness of the Saak transform are evident following the tests of dice similarity coefficients and various adversary perturbation algorithms, respectively. The addition of edge detection further allows for quantifying the surface area to volume ratio (SVR) of the myocardium in response to chemotherapy-induced cardiac remodeling.
Conclusion:The combination of Saak transform, random forest, and edge detection augments segmentation efficiency by 20-fold as compared to manual processing.Significance: This new methodology establishes a robust framework for post light-sheet imaging processing, and creating a data-driven machine learning for automated quantification of cardiac ultra-structure.
“…Manual segmentation of cardiac images in zebrafish and mice remains as the gold standard for ground truth despite being a labor-intensive and error-prone method. Currently existing automatic methods, including adaptive binarization, clustering, voronoi-based segmentation, and watershed, have remained limited [24]. For instance, adaptive histogram thresholding provides a semi-automated computational approach to perform image segmentation; however, the output is detracted by variability in background-to-noise ratio from the standard image processing algorithms [23].…”
AbstractRecent advances in light-sheet fluorescence microscopy (LSFM) enable 3-dimensional (3-D) imaging of cardiac architecture and mechanics in toto. However, segmentation of the cardiac trabecular network to quantify cardiac injury remains a challenge. We hereby employed “subspace approximation with augmented kernels (Saak) transform” for accurate and efficient quantification of the light-sheet image stacks following chemotherapy-treatment. We established a machine learning framework with augmented kernels based on the Karhunen-Loeve Transform (KLT) to preserve linearity and reversibility of rectification. The Saak transform-based machine learning enhances computational efficiency and obviates iterative optimization of cost function needed for neural networks, minimizing the number of training data sets to three 2-D slices for segmentation in our scenario. The integration of forward and inverse Saak transforms serves as a light-weight module to filter adversarial perturbations and reconstruct estimated images, salvaging robustness of existing classification methods. The accuracy and robustness of the Saak transform are evident following the tests of dice similarity coefficients and various adversary perturbation algorithms, respectively. The addition of edge detection further allows for quantifying the surface area to volume ratio (SVR) of the myocardium in response to chemotherapy-induced cardiac remodeling. The combination of Saak transform, random forest, and edge detection augments segmentation efficiency by 20-fold as compared to manual processing; thus, establishing a robust framework for post light-sheet imaging processing, creating a data-driven machine learning for 3-D quantification of cardiac ultra-structure.
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