Rapid, label-free, volumetric, and automated assessment in microscopy is necessary to assess the dynamic interactions between lymphocytes and their targets through the immunological synapse (IS) and the relevant immunological functions. However, attempts to realize the automatic tracking of IS dynamics have been stymied by the limitations of imaging techniques and computational analysis methods. Here, we demonstrate the automatic three-dimensional IS tracking by combining optical diffraction tomography and deep-learning-based segmentation. The proposed approach enables quantitative spatiotemporal analyses of IS regarding morphological and biochemical parameters related to its protein densities, offering a novel complementary method to fluorescence microscopy for studies in immunology.
The recent progress in machine learning, a subfield of artificial intelligence (AI) with a focus on learning algorithms, is attracting researchers in quantitative phase imaging (QPI). The fast and label-free nature of QPI is ideal for generating large-scale data to train supervised machine learning algorithms. The algorithms discover important structures in large, multidimensional training data to exploit them for augmenting new QPI measurements. Here, we present two major directions in synergistically combining QPI with AI, with a particular focus on a state-of-the-art machine learning technique called deep learning.
One direction is systematic exploitation of QPI data. Employing image classification frameworks, class-dependent characteristics encoded in the images are extracted for rapid diagnosis and screening. This approach has been demonstrated in a wide range of biological systems ranging from microbes to cells to tissues, with various modalities including 2D phase, 3D tomographic, time-lapse, and spectral measurements. In these methods, AI complements limited chemical specificity of QPI by maximally utilizing refractive index information in a data-driven manner.
The second direction is an improvement of QPI methods themselves. In computational side, efficient 2D holographic or 3D tomographic reconstruction was demonstrated using neural networks. For an experimental side, reinforcement learning frameworks are employed for efficient measurements in an adaptive fashion. This direction is relatively unexplored and provides a promising frontier.
We envision that these approaches would form an indispensable toolbox for QPI and facilitate exciting new applications. As QPI is extensively studied and commercialized, rapidly accumulating data for various biological systems would render the methods increasingly powerful.
We demonstrate that simultaneous application of optical clearing agents (OCAs) and complex wavefront shaping in optical coherence tomography (OCT) can provide significant enhancement of penetration depth and imaging quality. OCA reduces optical inhomogeneity of a highly scattering sample, and the wavefront shaping of illumination light controls multiple scattering, resulting in an enhancement of the penetration depth and signal-to-noise ratio. A tissue phantom study shows that concurrent applications of OCA and wavefront shaping successfully operate in OCT imaging. The penetration depth enhancement is further demonstrated for ex vivo mouse ears, revealing hidden structures inaccessible with conventional OCT imaging.