Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification  

作　　者：Rongrong Liu Shiyi Cheng Lei Tian Ji Yi 

机构地区：[1]Department of Biomedical Engineering,Northwestern University,Evanston,IL 60208,USA [2]Department of Electrical and Computer Engineering,Boston University,Boston,MA 02215,USA [3]Department of Biomedical Engineering,Boston University,Boston,MA 02215,USA [4]Department of Medicine,Boston University School of Medicine,Boston Medical Center,Boston,MA 02118,USA

出　　处：《Light(Science & Applications)》2019年第1期223-235,共13页光（科学与应用)（英文版）

基　　金：National Science Foundation(1813848);National Institute of Health(R01CA224911,R01CA232015,R01NS108464,R21EY029412);Bright Focus Foundation(G2017077,M2018132).

摘　　要：Measurement of blood oxygen saturation(sO_(2))by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism.Despite different embodiments and modalities,all label-free optical-imaging oximetry techniques utilize the same principle of sO_(2)-dependent spectral contrast from haemoglobin.Traditional approaches for quantifying sO_(2) often rely on analytical models that are fitted by the spectral measurements.These approaches in practice suffer from uncertainties due to biological variability,tissue geometry,light scattering,systemic spectral bias,and variations in the experimental conditions.Here,we propose a new data-driven approach,termed deep spectral learning(DSL),to achieve oximetry that is highly robust to experimental variations and,more importantly,able to provide uncertainty quantification for each sO_(2) prediction.To demonstrate the robustness and generalizability of DSL,we analyse data from two visible light optical coherence tomography(vis-OCT)setups across two separate in vivo experiments on rat retinas.Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra.Two neural-network-based models are tested and compared with the traditional least-squares fitting(LSF)method.The DSL-predicted sO_(2) shows significantly lower mean-square errors than those of the LSF.For the first time,we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment.Our DSL overcomes several limitations of traditional approaches and provides a more flexible,robust,and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.

关 键 词：DSL optical SCATTERING 

分 类 号：TN9[电子电信—信息与通信工程]