Small training dataset convolutional neural networks for application-specific super-resolution microscopy

Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to trai...

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Vydáno v:Journal of biomedical optics Ročník 28; číslo 3; s. 036501
Hlavní autoři: Mannam, Varun, Howard, Scott
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Society of Photo-Optical Instrumentation Engineers 01.03.2023
SPIE
S P I E - International Society for
Témata:
Animals
Artificial neural networks
Cattle
Coders
Computer architecture
Data interpretation
Datasets
Encoders-Decoders
Endothelial Cells
Fibroblasts
Fluorescence
Fluorescence microscopy
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image resolution
Information retrieval
Localization
Machine Learning
Magnetic Resonance Imaging - methods
Microscopy
Microscopy - methods
Neural networks
Neural Networks, Computer
Pulmonary arteries
Pulmonary artery
Research methodology
Signal to noise ratio
System effectiveness
Training
generative adversarial networks
fully convolutional networks
small datasets
super-resolution
dense encoder-decoder
biomedical imaging
diffraction-limited
convolutional neural networks
machine learning
fluorescence microscopy
dense layer
ISSN:1083-3668, 1560-2281, 1560-2281
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Popis
Shrnutí:Machine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need to acquire large datasets to train the neural network. We demonstrate how adding a "dense encoder-decoder" (DenseED) block can be used to effectively train a neural network that produces super-resolution (SR) images from conventional microscopy diffraction-limited (DL) images trained using a small dataset [15 fields of view (FOVs)]. The ML helps to retrieve SR information from a DL image when trained with a massive training dataset. The aim of this work is to demonstrate a neural network that estimates SR images from DL images using modifications that enable training with a small dataset. We employ "DenseED" blocks in existing SR ML network architectures. DenseED blocks use a dense layer that concatenates features from the previous convolutional layer to the next convolutional layer. DenseED blocks in fully convolutional networks (FCNs) estimate the SR images when trained with a small training dataset (15 FOVs) of human cells from the Widefield2SIM dataset and in fluorescent-labeled fixed bovine pulmonary artery endothelial cells samples. Conventional ML models without DenseED blocks trained on small datasets fail to accurately estimate SR images while models including the DenseED blocks can. The average peak SNR (PSNR) and resolution improvements achieved by networks containing DenseED blocks are and , respectively. We evaluated various configurations of target image generation methods (e.g., experimentally captured a target and computationally generated target) that are used to train FCNs with and without DenseED blocks and showed that including DenseED blocks in simple FCNs outperforms compared to simple FCNs without DenseED blocks. DenseED blocks in neural networks show accurate extraction of SR images even if the ML model is trained with a small training dataset of 15 FOVs. This approach shows that microscopy applications can use DenseED blocks to train on smaller datasets that are application-specific imaging platforms and there is promise for applying this to other imaging modalities, such as MRI/x-ray, etc.
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ISSN:1083-3668
1560-2281
1560-2281
DOI:10.1117/1.JBO.28.3.036501