.png){kind=icon}
返回
Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging
.png){kind=icon}
OA
AI
DOI：10.1109/TMI.2021.3091207.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Quantitative differential phase-contrast (qDPC) imaging is a label-free phase retrieval method for weak phase objects using asymmetric illumination. However, qDPC imaging with fewer intensity measurements leads to anisotropic phase distribution in reconstructed images. In order to obtain isotropic phase transfer function, multiple measurements are required; thus, it is a time-consuming process. Here, we propose the feasibility of using deep learning (DL) method for isotropic qDPC microscopy from the least number of measurements. We utilize a commonly used convolutional neural network namely U-net architecture, trained to generate 12-axis isotropic reconstructed cell images (i.e. output) from 1-axis anisotropic cell images (i.e. input). To further extend the number of images for training, the U-net model is trained with a patch-wise approach. In this work, seven different types of living cell images were used for training, validation, and testing datasets. The results obtained from testing datasets show that our proposed DL-based method generates 1-axis qDPC images of similar accuracy to 12-axis measurements. The quantitative phase value in the region of interest is recovered from 66% up to 97%, compared to ground-truth values, providing solid evidence for improved phase uniformity, as well as retrieved missing spatial frequencies in 1-axis reconstructed images. In addition, results from our model are compared with paired and unpaired CycleGANs. Higher PSNR and SSIM values show the advantage of using the U-net model for isotropic qDPC microscopy. The proposed DL-based method may help in performing high-resolution quantitative studies for cell biology.
Keyword:
Microscopy
Transfer functions
Imaging
Phase measurement
Image reconstruction
Biomedical measurement
Computer architecture
Coded-illumination
deep neural network
differential phase contrast
patch-based
quantitative phase imaging
U-net model
CycleGAN
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
6.3K
被引数:
3.7W
机构
引用论文
Optimal biologic dose of metronomic chemotherapy regimens is associated with maximum antiangiogenic activity.png)
BloodOAAI
BloodIF0
OAAI分享收藏The dynamics of aged airmasses and ozone in the Western Mediterranean: Relevance to forest ecosystemsChemosphere
ChemosphereIF0
PREAI
分享收藏分享收藏Towards a RINA-Based Architecture for Performance Management of Large-Scale Distributed SystemsComputersOAAI
ComputersIF0
OAAI分享收藏
查看更多内容