Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram

Abstract

Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However, unlike a conventional bright-field microscopy image, the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here, we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast, combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this “bright-field holography” method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging, and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram, benefiting from the wave-propagation framework of holography.

Deep learning‐based color holographic microscopy

“…For example, grayscale photographs/images have been colorized using generative adversarial networks (GANs) . As some other examples, holograms of objects (acquired at a single wavelength) have been reconstructed using deep neural networks with the color contrast of brightfield microscopy , and the images of unstained/label‐free tissue samples have been transformed into brightfield equivalent color images of the same samples, demonstrating virtual staining of label‐free tissue using holography or grayscale auto‐fluorescence images of tissue sections . In comparison with the traditional hyperspectral imaging approaches used in coherent imaging systems, the proposed deep neural‐network‐based color microscopy method of this work significantly simplifies the data acquisition procedures, the associated data processing and storage steps and the imaging hardware.…”

Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

“…Although the human capability of delivering large volumes of clinically relevant data exponentially increased over the last decade, the capacity of effectively analyzing such data did not, being naturally limited by the skills of the pathologists called to judge based on their own experience. Thus, biology research, diagnostics, and medicine naturally started relying on AI‐based cellular image analysis 147‐186 . AI largely extends the variety of tasks that image analysis can accomplish.…”

“…Thus, large volumes can be analyzed with remarkably low limit of detection ( in whole blood) 20 . Beside the abovementioned applications of AI for cells segmentation and sorting based on the input images, a new concept is emerging, referred to as augmented microscopy 15,183‐187 . With this term one refers to all the cases where deep learning is used to improve the performance of existing microscopes beyond their physical limitations 184 .…”

Misalignment resilient diffractive optical networks

“…Deep learning has been redefining the state-of-the-art for processing various signals collected and digitized by different sensors, monitoring physical processes for, e.g., biomedical image analysis [1][2][3][4], speech recognition [5,6] and holography [7][8][9][10], among many others [11][12][13][14][15][16][17]. Furthermore, deep learning and related optimization tools have been harnessed to find data-driven solutions for various inverse problems arising in, e.g., microscopy [18][19][20][21][22], nanophotonic designs and plasmonics [23][24][25].…”