Automated Stereology Using Deep Learning and Extended Depth of Field Imaging
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Solution Overview
Problem
Current stereology methods for determining cell characteristics in tissue samples are time- and labor-intensive, requiring manual data collection and adjustments, which hinders high-throughput applications due to the need for manual sectioning and object selection in biological samples with multiple cell types.
Innovation Solution
An automated stereology system using deep learning and extended depth of field (EDF) imaging, which captures Z-stacks of images, constructs EDF images, performs segmentation through Gaussian Mixture Models, morphological operations, and watershed segmentation to accurately estimate cell number and size in tissue sections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and adjustments are used in stereology methods, then measurement precision can be maintained, but productivity is significantly reduced due to time- and labor-intensive processes
Solution Approach 1:
The patent replaces manual mechanical operations (microscope focusing, object selection, data recording) with an automated image processing system that uses algorithms to perform segmentation, depth of field extension, and stereological calculations, thereby maintaining measurement precision while dramatically increasing productivity
Solution Approach 2:
The system enables self-service automation where the software automatically performs focus stacking, segmentations objects of interest, and calculates stereological parameters without requiring continuous manual intervention, allowing the system to process multiple tissue samples independently
2Productivity
If automated image processing is implemented, then productivity increases, but device complexity increases due to multiple processing steps
Solution Approach 1:
The patent merges multiple separate processing operations (focus stacking, segmentation, stereological measurement) into an integrated automated pipeline where each step feeds into the next, reducing the perceived complexity by presenting a unified interface while maintaining high productivity internally
3Measurement precision
If manual section thickness determination is performed, then measurement precision is maintained through careful adjustment, but loss of time increases due to repeated manual focusing
Solution Approach 1:
The system performs preliminary automated focus stacking to capture a series of images at different focal planes before the actual measurement process, establishing the depth information needed for accurate section thickness determination without requiring repeated manual focusing during analysis
Data Source
AI summary
Systems and methods for automated stereology are provided. In some embodiments, an active deep learning approach may be utilized to allow for a faster and more efficient training of a deep learning model for stereology analysis. In other embodiments, existing deep learning models for stereology analysis may be re-tuned to develop greater accuracy for a given data set of interest, either with or without an active deep learning approach. A method can include: capturing a data set including a stack of images of a three-dimensional (3D) object; determining whether an existing deep learning model is appropriate for use on the stack of images (or for re-tuning); performing pre-processing on the data set; performing a training of a deep learning model; applying the deep learning model to obtain a confidence score for each label of the data set; reviewing, by a user, at least some labels in the active set to verify whether the label displays sufficient agreement with an expected result, and moving only those that display sufficient agreement to a training set; and performing a stereology analysis using the trained deep learning model.