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Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation

Yichen Ding, Varun Gudapati, Ruiyuan Lin, Yanan Fei, Sibo Song, Chih-Chiang Chang, Kyung In Baek, Zhaoqiang Wang, Mehrdad Roustaei, Dengfeng Kuang, C.-C. Jay Kuo, Tzung K. Hsiai
doi: https://doi.org/10.1101/793182
Yichen Ding
1UCLA
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Varun Gudapati
1UCLA
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Ruiyuan Lin
2University of Southern California
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Yanan Fei
2University of Southern California
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Sibo Song
2University of Southern California
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Chih-Chiang Chang
1UCLA
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Kyung In Baek
1UCLA
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Zhaoqiang Wang
1UCLA
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Mehrdad Roustaei
1UCLA
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Dengfeng Kuang
3Nankai University
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C.-C. Jay Kuo
2University of Southern California
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Tzung K. Hsiai
1UCLA
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  • For correspondence: thsiai@mednet.ucla.edu
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Abstract

Recent 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted October 04, 2019.
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Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation
Yichen Ding, Varun Gudapati, Ruiyuan Lin, Yanan Fei, Sibo Song, Chih-Chiang Chang, Kyung In Baek, Zhaoqiang Wang, Mehrdad Roustaei, Dengfeng Kuang, C.-C. Jay Kuo, Tzung K. Hsiai
bioRxiv 793182; doi: https://doi.org/10.1101/793182
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Saak Transform-Based Machine Learning for Light-Sheet Imaging of Cardiac Trabeculation
Yichen Ding, Varun Gudapati, Ruiyuan Lin, Yanan Fei, Sibo Song, Chih-Chiang Chang, Kyung In Baek, Zhaoqiang Wang, Mehrdad Roustaei, Dengfeng Kuang, C.-C. Jay Kuo, Tzung K. Hsiai
bioRxiv 793182; doi: https://doi.org/10.1101/793182

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