Browsing by Author "Yang, Changye"

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    DINAVID: A Distributed and Networked Image Analysis System for Volumetric Image Data
    (Cold Spring Harbor Laboratory, 2022-05-11) Han, Shuo; Chen , Alain; Lee, Soonam; Fu, Chichen; Yang, Changye; Wu, Liming; Winfree, Seth; El-Achkar, Tarek M.; Dunn, Kenneth W.; Salama, Paul; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Background: The advancement of high content optical microscopy has enabled the acquisition of very large 3D image datasets. Image analysis tools and three dimensional visualization are critical for analyzing and interpreting 3D image volumes. The analysis of these volumes require more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based/cloud-based 3D image processing system. Results: The Distributed and Networked Analysis of Volumetric Image Data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. DINAVID is a server/cloud-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID is designed using open source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis as well as a 3D visualization system. Conclusions: In this paper, we will present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis.
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    Three Dimensional Nuclei Segmentation and Classification of Fluorescence Microscopy Images
    (IEEE, 2020-04) Han, Shuo; Lee, Soonam; Chen, Alain; Yang, Changye; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
    Segmentation and classification of cell nuclei in fluorescence 3D microscopy image volumes are fundamental steps for image analysis. However, accurate cell nuclei segmentation and detection in microscopy image volumes are hampered by poor image quality, crowding of nuclei, and large variation in nuclei size and shape. In this paper, we present an unsupervised volume to volume translation approach adapted from the Recycle-GAN using modified Hausdorff distance loss for synthetically generating nuclei with better shapes. A 3D CNN with a regularization term is used for nuclei segmentation and classification followed by nuclei boundary refinement. Experimental results demonstrate that the proposed method can successfully segment nuclei and identify individual nuclei.