3D Optical Microscopy Processing with Automated ROI Imaging
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Solution Overview
Problem
Current 3D microscopy techniques face challenges such as image artifacts due to signal intensity drop-off and non-specific staining, leading to inaccurate analysis of tissue structures like liver fibrosis and steatosis, particularly in large datasets requiring manual input and high computational resources.
Innovation Solution
A system and method for processing 3D microscopy data using parallel processing of low and high resolution images, automated ROI identification, and machine learning to enhance image analysis, reducing manual labor and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D microscopy is used to acquire high-resolution image data from tissue samples, then measurement precision is improved, but device complexity and computational resources increase
Solution Approach 1:
The patent segments the 3D microscopy system into multiple independent components: illumination optics, collection optics, and detector systems. Each component can be optimized separately and the system can be configured with different combinations of objectives and detectors based on specific imaging needs, reducing overall system complexity while maintaining high resolution capability.
Solution Approach 2:
The patent transitions from traditional 2D imaging to 3D volumetric imaging by adding the z-axis dimension through optical sectioning. This is achieved using light sheet microscopy where a thin sheet of light illuminates the sample at different depths, allowing reconstruction of 3D structures from 2D slices without requiring a fully three-dimensional scanning system.
2Measurement precision
If 3D microscopy is used to acquire high-resolution image data from tissue samples, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent enables continuous imaging through the light sheet approach, where the illumination and collection optics can simultaneously capture data from different depths of the sample without interruption. This continuous acquisition method eliminates the need for sequential scanning at each depth, significantly reducing total acquisition time while maintaining high resolution.
Solution Approach 2:
The system uses periodic scanning of the light sheet through the sample volume, allowing rapid sequential imaging at different z-positions. This periodic illumination pattern enables efficient data collection across the entire 3D volume without requiring continuous mechanical movement, reducing acquisition time while preserving image quality.
3Ease of operation
If automated ROI identification is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service through automated image processing algorithms that automatically identify regions of interest, segment tissues, and quantify features without requiring manual intervention. The system processes images through multiple computational steps including denoising, thresholding, and morphological operations to autonomously complete analysis tasks that would otherwise require expert manual review.
Solution Approach 2:
The automated ROI identification system incorporates feedback mechanisms where the initial processing results are reviewed and adjusted, and the system learns from user corrections to improve future automated detections. This feedback loop allows the system to maintain high accuracy in automated analysis while adapting to different tissue types and staining patterns without requiring reprogramming.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient collection and analysis of complex biological structures, improving diagnostic accuracy and reducing computing power and time, while preserving tissue for molecular tests.
Implementation Method 1
an imaging device configured to image a sample
Implementation Method 2
illumination optics which direct the illumination light onto a tissue sample
Data Source
AI summary
Systems and methods for capturing and processing image data captured by an optical microscope, such as an Open Top Light Sheet (OTLS) microscope are disclosed. Image data can be processed by techniques including flat fielding, image depth correction, and edge correction, and pixel classification and image segmentation techniques can be applied to more accurately identify lipid droplets and fibrous structures for assessment of, for example, steatosis and fibrosis in human liver biopsy tissue samples. Systems and methods are also related to capturing low resolution images of a sample, identifying regions of interest, and then capturing high resolution image of the regions of interest to reduce memory used for storage, increase computation speed, and reduce computational power.