Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis

Abstract

Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.

Deep learning-enabled virtual histological staining of biological samples

“…Different types of stains can be simultaneously generated at the same tissue cross-section using a single (or multiple) virtual staining model(s) to provide additional histological information that aids the diagnostic evaluation 21 . This additional histological information was also proven to improve the performance of other downstream machine vision tasks in digital pathology, such as the detection or segmentation of pathological signatures 27,50,53,55,58 and classification of malignancies 32,39,91 . By allowing different stains to be performed on the same tissue section, more tissue will be preserved and be available in diagnostically challenging cases for ancillary tests (e.g., DNA/RNA sequencing) that may be required to reach a diagnosis.…”

“…c Blind inference of a trained virtual staining model. The virtual histology images are rapidly generated from label-free images using a digital computer smears 39 , as well as H&E 40 and IHC 41 staining on prostate tissue sections; photoacoustic microscopy was also demonstrated to achieve virtual H&E staining of mouse brain 42 and frozen sections of bone tissue 43 . As another example, Mayerich.…”

“…For instance, to avoid intensity variations caused by potential photobleaching in autofluorescence imaging, Rivenson et al normalized the input autofluorescence images by subtracting the mean value across the entire tissue slide and dividing it by the standard deviation of the pixel values 18 . Alternatively, to mitigate these variations and enhance the image contrast, some works saturated the top 1% and the bottom 1% of the pixels 38,39 . Pradhan et al also reported that normalizing label-free nonlinear multi-modal (NLM) images in a pixel value range from -1 to 1 could avoid large number multiplications during the training process, helping with better network convergence 35 .…”

“…More importantly, these automated image comparison and evaluation tools are fully scalable to be used in large-scale studies, potentially eliminating a bottleneck due to the limited availability of pathologists. For example, Kaza et al 39 trained a classification model to distinguish the dead and viable cells and their subtypes, which was used to validate the performance of a virtual staining model. Similarly, Li et al 26 trained a downstream CNN for colonic gland segmentation to demonstrate that the virtually stained images preserve the same rich histopathological information as the histochemically stained ones.…”

Deep-UV microscopy as a tool to capture intracellular dynamics

“…Multispectral UV microscopy has also been used for label-free prostate tissue characterization 13 . Recently, UV microscopy has been demonstrated for robust hematology analysis including automated classification of various WBC subtypes and pseudo-colorization of blood smear images to mimic hematological staining 11,12,14,15 . While these studies have used static multispectral UV microscopy images, existing setups can operate at speeds exceeding 1kHz, making them a viable tool for multiscale dynamic imaging as well.…”