人工智能
稳健性(进化)
计算机科学
深度学习
分割
图像处理
图像分割
子空间拓扑
计算机视觉
算法
模式识别(心理学)
机器学习
图像(数学)
生物化学
基因
化学
作者
Yichen Ding,Varun Gudapati,Ruiyuan Lin,Yanan Fei,René R. Sevag Packard,Shang Song,Chih-Chiang Chang,Kyung In Baek,Zhaoqiang Wang,M H Roustaei,Dengfeng Kuang,C.-C. Jay Kuo,Tzung K. Hsiai
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:68 (1): 225-235
被引量:15
标识
DOI:10.1109/tbme.2020.2991754
摘要
Objective: 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. 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.
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