Deep learning-based solutions for electron microscopy image analysis
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Electron microscopy (EM) enables capturing high resolution images of very small structures in biological and non-biological specimens such as membrane proteins, viruses, subcellular structures, nanoparticles, or material surfaces. Electron microscopy plays a critical role in research, development, and diagnosis in many applications of biological, physical, chemical and material sciences. Thanks to advances in instrumentation, electron microscopy generates large amounts of complex data that is no longer feasible to analyze manually. There is a growing need for development of computational methods and tools for automated analysis of electron microscopy data generated for variety of research fields. Recent advances in artificial intelligence and machine learning, particularly in deep learning have revolutionized image processing and computer vision. In this work, we explored deep learning guided image processing and computer vision solutions to address the growing high-performance processing needs of image data acquired using electron microscopy. The proposed solutions involved novel multi-step, 2D/3D fusion approaches to address the unique challenges of complex, low-contrast, noisy electron microscopy imagery; and selfsupervised, semi-supervised, or meta-learning schemes to address the challenges caused by lack of or limited amounts of labeled training data. These image analysis solutions were used for detection, segmentation, and quantification of various biological structures of interest such as proteins, viruses, mitochondrial or neural structures; and non-biological structures of interest such as carbon nanotube forests. Experiments conducted on the proposed methods showed robust and promising results towards automated, objective, and quantitative analysis of electron microscopy image data, that is of great value for biology, medicine, and material science applications.
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Ph. D.