Aberration correction method for Fourier ptychographic microscopy based on neural network

2020 ◽  
Author(s):  
Jinlei Zhang ◽  
Xiao Tao ◽  
Chenning Tao ◽  
Peng Sun ◽  
Zhanghao Ding ◽  
...  
Keyword(s):  
Neural Network ◽  
Correction Method ◽  
Aberration Correction

scholarly journals Study on Aberration Correction of Adaptive Optics Based on Convolutional Neural Network

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 377
Author(s):  
Jin Li ◽  
Luwei Wang ◽  
Yong Guo ◽  
Yangrui Huang ◽  
Zhigang Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Adaptive Optics ◽  
Correction Method ◽  
Limiting Factor ◽  
Aberration Correction ◽  
Microscopy Imaging ◽  
Imaging Resolution ◽  
Short Time ◽  
The Relationship

The existence of aberrations has always been an important limiting factor in the imaging field. Especially in optical microscopy imaging, the accumulated aberration of the optical system and the biological samples distorts the wavefront on the focal plane, thereby reducing the imaging resolution. Here, we propose an adaptive optical aberration correction method based on convolutional neural network. By establishing the relationship between the Zernike polynomial and the distorted wavefront, with the help of the fast calculation advantage of an artificial intelligence neural network, the distorted wavefront information can be output in a short time for the reconstruction of the wavefront to achieve the purpose of improving imaging resolution. Experimental results show that this method can effectively compensate the aberrations introduced by the system, agarose and HeLa cells. After correcting, the point spread function restored the doughnut-shape, and the resolution of the HeLa cell image increased about 20%.

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

scholarly journals Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 32 ◽  
Author(s):  
Chuan-Pin Lu ◽  
Jiun-Jian Liaw ◽  
Tzu-Ching Wu ◽  
Tsung-Fu Hung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Economic Benefit ◽  
Nutritional Value ◽  
Production Management ◽  
Correction Method ◽  
Agricultural Product ◽  
Image Measurement ◽  
Growth Measurement ◽  
Affected Plant

In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms.

scholarly journals Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2957 ◽  
Author(s):  
Gihyoun Lee ◽  
Sang Jin ◽  
Jinung An
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Motion Artifact ◽  
Correction Method ◽  
Motion Artifacts ◽  
Functional Near Infrared Spectroscopy ◽  
Artifact Correction ◽  
Performance Evaluation Index

In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

WE-G-211-02: A Fast Aberration Correction Method for 3D Reconstruction with Confocal Microscopic Images

2011 ◽  
Vol 38 (6Part33) ◽  
pp. 3835-3835
Author(s):  
Y Zhang ◽  
X Hu ◽  
P Wang ◽  
Y Yang ◽  
R Niu ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Correction Method ◽  
Aberration Correction ◽  
Microscopic Images
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