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Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network

机译：基于条件生成对抗网络的RGB图像和稀疏高光谱信号估计组织氧饱和度

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摘要

PurposeIntra-operative measurement of tissue oxygen saturation (

{StO}_{2}

) is important in detection of ischaemia, monitoring perfusion and identifying disease. Hyperspectral imaging (HSI) measures the optical reflectance spectrum of the tissue and uses this information to quantify its composition, including

{StO}_{2}

. However, real-time monitoring is difficult due to capture rate and data processing time.

机译：目的术中测量组织氧饱和度（<数学xmlns：mml =“ http://www.w3.org/1998/Math/MathML” id =“ M2”溢出=“ scroll”> StO 2 ）对于检测缺血，监测灌注和识别疾病非常重要。高光谱成像（HSI）测量组织的光学反射光谱，并使用此信息来量化其组成，包括

StO2。但是，由于捕获率和数据处理时间的原因，实时监控很困难。

著录项

期刊名称 Springer Open Choice
作者
Qingbiao Li; Jianyu Lin; Neil T. Clancy; Daniel S. Elson;
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作者单位

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年(卷),期 -1(14),6
年度 -1
页码 987–995
总页数 9
原文格式 PDF
正文语种
中图分类外科学;
关键词
Intro-operative imaging Optical imaging Tissue oxygen saturation Generative adversarial network;

机译：术中成像;光学成像;组织氧饱和度;对抗性生成网络;

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专利

1. GENERATING ARTIFICIAL NEAR INFRARED SPECTRAL BAND FROM RGB IMAGE USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK [J] . X. Yuan, J. Tian, P. Reinartz ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences . 2020,第5期

机译：使用条件生成对抗网络从RGB图像生成人工近红外光谱带
2. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose [J] . Wang Yan, Yu Biting, Wang Lei, NeuroImage . 2018,第期

机译：用于低剂量的高质量PET图像估计的3D条件生成对抗网络
3. Classification of Hyperspectral Images Based on Multiclass Spatial–Spectral Generative Adversarial Networks [J] . Feng Jie, Yu Haipeng, Wang Lin, IEEE Transactions on Geoscience and Remote Sensing . 2019,第8期

机译：基于多类空间谱生成对抗网络的高光谱图像分类
4. Towards Virtual HE Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks [C] . Neslihan Bayramoglu, Mika Kaakinen, Lauri Eklund, IEEE International Conference on Computer Vision Workshops . 2017

机译：使用条件生成对抗网络实现高光谱肺组织学图像的虚拟H＆E染色
5. Semantic Segmentation for Producing Nuclei Stained Images Using Conditional Generative Adversarial Networks [D] . Bhatia, Tanmay . 2019

机译：使用条件生成对抗性网络产生核染色图像的语义分割
6. 3D conditional generative adversarial networks for high-quality PET image estimation at low dose [O] . Yan Wang, Biting Yu, Lei Wang, -1

机译：低剂量3D条件生成对抗性网络用于高质量PET图像估计
7. Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology [O] . Martin Halicek, Samuel Ortega, Himar Fabelo, 2020

机译：从数字化组织学中综合乳腺癌细胞高光谱图像的条件生成的对抗网络

Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network

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