Virtual Staining of Digital Holographic Microscopy Images
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Solution Overview
Problem
Digital holographic microscopy (DHM) images lack sufficient detail and resolution for efficient identification and categorization of white blood cell types, making manual staining processes time-consuming and labor-intensive, and it is difficult to train automated systems to identify virtually-stained cells due to the unavailability of paired stained and DHM images.
Innovation Solution
A computer-implemented method using generative adversarial networks with cycle consistency to virtually stain DHM images, trained with unpaired data sets of DHM and stained white blood cell images, to produce images that imitate the appearance of manually stained cells, enabling efficient cell type identification and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual staining process is used to enhance cell image detail and resolution, then cell type identification accuracy is improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent creates virtual stained images by copying and transforming DHM images through generative adversarial networks. The GAN learns the mapping between DHM images and stained images, generating synthetic stained images that replicate the visual characteristics of manual staining without requiring actual staining procedures. This copying approach preserves identification accuracy while eliminating time-consuming manual processes.
Solution Approach 2:
The patent replaces the mechanical staining process with a computational system. Instead of using physical dyes and manual microscopy procedures, the system uses deep learning algorithms (GANs) to transform DHM images into stained-like images. This substitution eliminates the need for physical staining materials and manual operations while maintaining the diagnostic value of stained images.
2Measurement precision
If manual staining process is used to enable cell type differentiation, then diagnostic capability is improved, but labor intensity increases
Solution Approach 1:
The system copies the visual appearance of stained cells through virtual staining. The GAN generates synthetic stained images that replicate the color patterns and structural details necessary for cell type differentiation. This copying mechanism provides diagnostic capability without requiring clinicians to perform or review manual staining procedures, significantly reducing labor intensity.
Solution Approach 2:
The system enables self-service by automatically generating stained images from DHM images without human intervention in the staining process. The GAN performs the entire transformation autonomously, eliminating the need for manual staining operations and making the process easily operable with minimal labor input while maintaining diagnostic quality.
3Measurement precision
If paired stained and DHM images are used for training automated systems, then training accuracy is improved, but data acquisition complexity increases due to unavailability of paired data
Solution Approach 1:
The patent inverts the traditional training approach by using unpaired datasets. Instead of requiring paired DHM and stained images, the system trains the GAN on two separate unpaired datasets: one containing DHM images and another containing stained images. The GAN learns the transformation by comparing the statistical properties and features of these unpaired datasets, eliminating the complex requirement for paired data acquisition while still achieving accurate training.
Solution Approach 2:
The system achieves universal training capability by working with unpaired datasets from different sources. The GAN framework is designed to learn from independent datasets without requiring direct correspondence between training pairs. This multi-functionality allows the system to be trained on readily available unpaired data from various laboratories and sources, significantly reducing data acquisition complexity while maintaining training effectiveness.
Data Source
AI summary
A cell visualization system includes a digital holographic microscopy (DHM) device, a training device, and a virtual staining device. The DHM device produces DHM images of cells and the virtual staining device colorizes the DHM images based on an algorithm generated by the training device using generative adversarial networks and unpaired training data. A computer-implemented method for producing a virtually stained DHM image includes acquiring an image conversion algorithm which was trained using the generative adversarial networks, receiving a DHM image with depictions of one or more cells and virtually staining the DHM image by processing the DHM image using the image conversion algorithm. The virtually stained DHM image includes digital colorization of the one or more cells to imitate the appearance of a corresponding actually stained cell.