Nucleinet: an encoder-decoder convolutional neural network for nuclei image denoising
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 b...
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| Format: | Theses and Dissertations |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10356/72577 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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 |
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