3D Tissue Reconstruction via Computational Serial Sectioning
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Solution Overview
Problem
Current methods for visualizing and quantifying dense tissue structures in three-dimensional space are limited, particularly in large volumes and complex tissues like pancreatic cancer, where existing techniques face challenges with tissue clearing, antibody penetration, and cost-effective labeling.
Innovation Solution
The development of computational pipelines that utilize nonlinear image registration, color deconvolution, filtering, and deep learning semantic segmentation to generate multi-labelled digital 3D maps of tissue, allowing for single-cell analysis and visualization of tissue volume matrices at cm-scale with μm and single-cell resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If tissue clearing methods are used to visualize dense tissue structures in 3D space, then transparency and visualization capability are improved, but tissue size is limited and antibody penetration into dense stroma becomes poor
Solution Approach 1:
The patent creates a digital 3D copy of the tissue structure through serial section imaging and computational reconstruction, avoiding the need to physically clear the entire tissue volume. This digital replica preserves all structural information without the physical limitations of tissue clearing methods.
Solution Approach 2:
The tissue is divided into multiple thin serial sections that are imaged individually, then computationally reassembled into a 3D reconstruction. This segmentation approach allows analysis of large tissue volumes without requiring complete optical clearing of the entire sample.
2Measurement precision
If manual annotations and IHC labeling are used to identify tissue components in serial sections, then labeling accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs automated image analysis and tissue component identification using computational algorithms and deep learning models, eliminating the need for manual annotation by researchers. The computer system independently processes and labels tissue structures across all serial sections.
Solution Approach 2:
Manual IHC labeling and annotation processes are replaced with computational image analysis methods. Deep learning models automatically identify and classify tissue components, substituting wet lab techniques with in-silico analysis that is both faster and more consistent.
3Temperature
If serial sectioning is used to reconstruct 3D tissue architecture, then 3D visualization capability is improved, but section alignment and registration become complex and time-consuming
Solution Approach 1:
The patent introduces fiducial markers and computational registration algorithms as intermediaries to align serial sections. These tools facilitate precise matching of corresponding structures across multiple sections without requiring complex manual alignment procedures.
Solution Approach 2:
The system transforms the registration problem from a manual, geometry-based alignment task to an automated image-based matching process. By changing the approach from physical landmark matching to computational image correlation, the complexity of section alignment is significantly reduced.
Data Source
AI summary
This document generally describes methods and systems for generating digital reconstructions of tissue from humans or other species. The method can, for example, include receiving, at a computing system, image data of a tissue sample, where one or more sections of the tissue sample are stained with hematoxylin and eosin (H&E), registering the image data to generate registered image data, identifying tissue subtypes based on application of a machine learning model to the registered image data, annotating the identified tissue subtypes to generate annotated image data, and determining a digital volume of the tissue sample in three dimensional (3D) space based on the annotated image data. The disclosed technology can provide for single-cell analysis and other analysis of tissue samples, such as early detection of cancer in human tissue samples.