HoloStain: Holographic virtual staining of individual biological cells

Abstract

Many medical and biological protocols for analyzing individual biological cells involve morphological evaluation based on cell staining, designed to enhance imaging contrast and enable clinicians and biologists to differentiate between various cell organelles. However, cell staining is not always allowed in certain medical procedures. In other cases, staining may be time consuming or expensive to implement. Here, we present a new deep-learning approach, called HoloStain, which converts images of isolated biological cells acquired without staining by holographic microscopy to their virtually stained images. We demonstrate this approach for human sperm cells, as there is a well-established protocol and global standardization for characterizing the morphology of stained human sperm cells for fertility evaluation, but, on the other hand, staining might be cytotoxic and thus is not allowed during human in vitro fertilization (IVF). We use deep convolutional Generative Adversarial Networks (DCGANs) with training that is based on both the quantitative phase images and two gradient phase images, all extracted from the digital holograms of the stain-free cells, with the ground truth of bright-field images of the same cells that subsequently underwent chemical staining. To validate the quality of our virtual staining approach, an experienced embryologist analyzed the unstained cells, the virtually stained cells, and the chemically stained sperm cells several times in a blinded and randomized manner. We obtained a 5-fold recall (sensitivity) improvement in the analysis results. With the introduction of simple holographic imaging methods in clinical settings, the proposed method has a great potential to become a common practice in human IVF procedures, as well as to significantly simplify and facilitate other cell analyses and techniques such as imaging flow cytometry.

Submitted

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Cited by in corpus (15)

• Deep Learning-enabled Virtual Histological Staining of Biological Samples
• On the use of deep learning for phase recovery
• Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments
• Self-supervised learning of hologram reconstruction using physics consistency
• Deep learning in biomedical optics
• Virtual histological staining of unlabeled autopsy tissue
• Digital staining in optical microscopy using deep learning -- a review
• Extracting quantitative biological information from brightfield cell images using deep learning
• High space-bandwidth in quantitative phase imaging using partially spatially coherent optical coherence microscopy and deep neural network
• A robust and scalable framework for hallucination detection in virtual tissue staining and digital pathology
• High-resolution label-free imaging of tissue morphology with confocal phase microscopy
• Automated Whole Slide Imaging for Label-Free Histology using Photon Absorption Remote Sensing Microscopy
• Multiscale assay of unlabeled neurite dynamics using phase imaging with computational specificity (PICS)
• Tailored 3D microphantoms: an essential tool for quantitative phase tomography analysis of organoids
• Learning with minimal effort: leveraging in silico labeling for cell and nucleus segmentation