Chen Haitao, Liu Kaixian, Jiang Yuxuan, Liu Yafeng, Deng Yong
School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China.
Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China.
Biomed Opt Express. 2023 Mar 30;14(4):1818-1832. doi: 10.1364/BOE.489079. eCollection 2023 Apr 1.
Double integrating sphere measurements obtained from thin tissues provides more spectral information and hence allows full estimation of all basic optical properties (OPs) theoretically. However, the ill-conditioned nature of the OP determination increases excessively with the reduction in tissue thickness. Therefore, it is crucial to develop a model for thin tissues that is robust to noise. Herein, we present a deep learning solution to precisely extract four basic OPs in real-time from thin tissues, leveraging a dedicated cascade forward neural network (CFNN) for each OP with an additional introduced input of the refractive index of the cuvette holder. The results show that the CFNN-based model enables accurate and fast evaluation of OPs, as well as robustness to noise. Our proposed method overcomes the highly ill-conditioned restriction of OP evaluation and can distinguish the effects of slight changes in measurable quantities without any knowledge.
从薄组织获得的双积分球测量提供了更多光谱信息，因此理论上允许对所有基本光学特性（OPs）进行全面估计。然而，随着组织厚度的减小，OP测定的病态性质会过度增加。因此，开发一种对噪声具有鲁棒性的薄组织模型至关重要。在此，我们提出一种深度学习解决方案，利用针对每个OP的专用级联前向神经网络（CFNN）以及比色皿支架折射率的额外引入输入，从薄组织中实时精确提取四种基本OPs。结果表明，基于CFNN的模型能够对OPs进行准确快速的评估，并且对噪声具有鲁棒性。我们提出的方法克服了OP评估的高度病态限制，并且能够在无需任何先验知识的情况下区分可测量量的微小变化所产生的影响。
