Borhani Navid, Bower Andrew J, Boppart Stephen A, Psaltis Demetri
Optics Laboratory, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland.
Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Biomed Opt Express. 2019 Feb 19;10(3):1339-1350. doi: 10.1364/BOE.10.001339. eCollection 2019 Mar 1.
Deep neural networks have been used to map multi-modal, multi-photon microscopy measurements of a label-free tissue sample to its corresponding histologically stained brightfield microscope colour image. It is shown that the extra structural and functional contrasts provided by using two source modes, namely two-photon excitation microscopy and fluorescence lifetime imaging, result in a more faithful reconstruction of the target haematoxylin and eosin stained mode. This modal mapping procedure can aid histopathologists, since it provides access to unobserved imaging modalities, and translates the high-dimensional numerical data generated by multi-modal, multi-photon microscopy into traditionally accepted visual forms. Furthermore, by combining the strengths of traditional chemical staining and modern multi-photon microscopy techniques, modal mapping enables label-free, non-invasive studies of tissue samples or intravital microscopic imaging inside living animals. The results show that modal co-registration and the inclusion of spatial variations increase the visual accuracy of the mapped results.
深度神经网络已被用于将无标记组织样本的多模态、多光子显微镜测量结果映射到其相应的组织学染色明场显微镜彩色图像。结果表明，使用两种源模式（即双光子激发显微镜和荧光寿命成像）提供的额外结构和功能对比度，能够更忠实地重建目标苏木精和伊红染色模式。这种模态映射程序可以帮助组织病理学家，因为它提供了获取未观察到的成像模态的途径，并将多模态、多光子显微镜产生的高维数值数据转化为传统上可接受的视觉形式。此外，通过结合传统化学染色和现代多光子显微镜技术的优势，模态映射能够对组织样本进行无标记、非侵入性研究或对活体动物进行体内显微镜成像。结果表明，模态共配准和空间变化的纳入提高了映射结果的视觉准确性。
