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. Furthermore, staining protocols may be operator-sensitive, and hence lead to varying analytical results by different users, as well as cause artificial imaging artifacts or false heterogeneity. 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. After the training stage, the deep neural network can take images of unseen sperm cells, retrieved from the coinciding holograms acquired without staining, and convert them to their stain-like images. 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, demonstrating the advantage of using virtual staining for sperm cell analysis. 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

Refractive Index Changes of Cells and Cellular Compartments Upon Paraformaldehyde Fixation Acquired by Tomographic Phase Microscopy

“…, for remote diagnostics (7) as well as for novel methods of enhanced 3D RI distribution determination and analysis by use of artificial intelligence (AI) or machine learning (10)(11)(12).…”

Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure

“…A similar image-to-image translation estimation was used in ref. 60 to perform synthetic staining on sperm cells. While those proposals rely on the use of chemical-based markers to train the neural network, in this work, we avoid stains altogether and instead rely on SLIM's ability to directly observe cell ultrastructure.…”

“…To improve the segmentation accuracy, we used a two-pass training procedure where an initial training round was corrected and used for a second, final round. Manual annotation for the second round is comparably fast, which also allows us to correct for debris and other forms of clearly defective segmentation (60). To obtain the dry mass, we ran inference using our newly trained network.…”

Label-free bone marrow white blood cell classification using refractive index tomograms and deep learning

“…Recent advances in artificial intelligence (AI) have suggested unexplored domains of QPI beyond simply characterizing biological samples. As datasets obtained from QPI do not rely on the variability of staining quality, various machine learning and deep learning approaches can exploit uniform-quality and high-dimensional datasets to perform label-free image classification (Chen et al 2016; Jo et al 2017; Nissim et al 2020; Ozaki et al 2019; Wang et al 2020; Wu et al 2020; Yoon et al 2017; Zhang et al 2020; Zhou et al 2020) and inference (Chang et al 2020; Choi et al 2019; Dardikman-Yoffe et al 2020; Kandel et al 2020; Lee et al 2019; Nguyen et al 2018; Nygate et al 2020; Pitkäaho et al 2019; Rivenson et al 2018). Such synergetic approaches for label-free blood cell identification have also been demonstrated, which are of interest to this work (Go et al 2018; Kim et al 2019; Nassar et al 2019; Ozaki et al 2019; Singh et al 2020; Yoon et al 2017).…”