Si Lu, Li Naiqi, Huang Tongyu, Du Shan, Dong Yang, Yao Yue, Ma Hui
Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China.
Shenzhen International Graduate School, Tsinghua University, Shenzhen, China.
J Biophotonics. 2022 Mar;15(3):e202100242. doi: 10.1002/jbio.202100242. Epub 2021 Dec 8.
Mueller matrix (MM) polarimetry can provide comprehensive information about the polarization properties that are closely related to the microstructural features and has demonstrated its potential in biomedical studies and clinical practices, and bright-field microscopy is widely used in pathological diagnosis as the golden standard. In this work, we improve the throughput of MM microscopy by learning a statistical transformation between these two imaging systems based on deep learning. Using this approach, the MM microscope can generate an image that is equivalent to a bright-field microscope image of the matching field of view. We add new transformative capability to the existing MM imaging system without requiring extra hardware. The translation model is based on conditional generative adversarial network with customized loss functions. We demonstrated the effectiveness of our approach on liver and breast tissues and evaluated the performance by four quantitative similarity assessment methods in pixel, image and distribution levels, respectively.
穆勒矩阵（MM）偏振成像能够提供与微观结构特征密切相关的偏振特性的全面信息，并已在生物医学研究和临床实践中展现出其潜力，而明场显微镜作为金标准被广泛应用于病理诊断。在这项工作中，我们通过基于深度学习学习这两种成像系统之间的统计变换来提高MM显微镜的通量。使用这种方法，MM显微镜可以生成与匹配视野的明场显微镜图像等效的图像。我们在无需额外硬件的情况下为现有的MM成像系统增添了新的变换能力。该转换模型基于具有定制损失函数的条件生成对抗网络。我们在肝脏和乳腺组织上证明了我们方法的有效性，并分别通过像素、图像和分布层面的四种定量相似性评估方法评估了性能。
