METHOD AND SYSTEM FOR DIGITAL STAINING OF MICROSCOPY IMAGES USING DEEP LEARNING

Smart overview of the Invention

A novel digital staining method leverages deep learning to generate virtually stained microscopy images from unstained or label-free samples. The approach utilizes fluorescence lifetime imaging (FLIM) captured via a fluorescence microscope, transforming these inputs into digitally stained outputs that mimic traditional histochemically stained images. This technique is significant in the fields of pathology and biological sciences, providing an alternative to conventional staining methods that often involve complex processes and irreversible alterations to tissue samples.

Technical Field

The method pertains to imaging techniques for unstained tissue, specifically using deep neural networks to apply digital staining. It involves using machine learning algorithms to convert label-free tissue sections into images comparable to those obtained from traditional staining methods. This process aids in maintaining the integrity of the samples while still providing diagnostically useful images.

Background

Traditional microscopic imaging involves labor-intensive processes like formalin fixation and paraffin embedding, followed by staining and mounting on slides for microscopic examination. These steps require multiple reagents and can alter the tissue irreversibly. Alternative imaging methods have been explored, such as non-linear microscopy and multi-modal chemical analysis, but these often demand sophisticated equipment and extensive scanning times. The proposed method addresses these challenges by using endogenous fluorescence signals for digital staining.

Embodiments

• Deep Neural Network Training: A trained neural network utilizes matched immunohistochemistry (IHC) stained images and their corresponding FLIM images to generate virtually stained outputs that resemble IHC-stained samples.
• Virtual Autofocusing: The system improves image focus using a deep neural network trained with pairs of out-of-focus and in-focus images, enhancing the clarity of microscope images.
• Multiple Stains Representation: The network can generate virtually stained images with multiple stains, closely matching chemically stained counterparts by using class conditional matrices during processing.

Applications

This digital staining system offers a significant advancement in microscopy by reducing reliance on chemical reagents and preserving sample integrity. It provides flexibility in generating multiple stain types digitally, enabling pathologists to make diagnostic decisions based on digitally enhanced images that are equivalent to those produced by traditional methods. Furthermore, it offers potential improvements in workflow efficiency and accessibility of advanced imaging techniques in various research and clinical settings.