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

Rongrong Liu; Shiyi Cheng; Lei Tian; Ji Yi

doi:10.1038/s41377-019-0216-0

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

Article

- Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification
- Light: Science & Applications Vol. 8, Issue 6, Pages: 1020-1032(2019)
- Affiliations：
  
  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
- Author bio：
  
  Ji Yi (jiyi@bu.edu)
- Funds：
- DOI：10.1038/s41377-019-0216-0
  CLC：
- Published：2019，
  
  Published Online：20 November 2019，
  
  Received：08 July 2019，
  
  Revised：17 October 2019，
  
  Accepted：01 November 2019
- Accepted：
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Liu, R. R. et al. Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification. Light: Science & Applications, 8, 1020-1032 (2019).
DOI：

Liu, R. R. et al. Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification. Light: Science & Applications, 8, 1020-1032 (2019). DOI： 10.1038/s41377-019-0216-0.

摘要

Abstract

Measurement of blood oxygen saturation (

) 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

-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying

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

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 spectr

a. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted

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

Keywords

references

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