Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72577
Title: Nucleinet: an encoder-decoder convolutional neural network for nuclei image denoising
Authors: Hu, Yifei
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Abstract: Scalable image data analysis is widely demanded in biomedical diagnosis by leveraging rapidly developed optical technology and advanced machine learning algorithm. However, bio-image obtained for single molecular or cell always have additive and multiplicative noise and requires denoising with better resolution in diagnosis. This dissertation proposed a high-throughput bioimage denoising method for different kinds of threedimensional microscopy cell images. Using a convolutional encoderdecoder network, one can provide a scalable bio-image platform, called NucleiNet, to automatically segment, classify and track cell nuclei. Using a benchmark of 2480 nuclei images, the experiment results show that the network achieves a 0.98 F-score and 0.99 pixel-wise accuracy, which means that over 95% of nuclei were successfully detected with no merging nuclei found. Key words: Image denoising, Convolutional neural network, Machine learning, Bio-image processing
URI: http://hdl.handle.net/10356/72577
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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