3D Disector Cell Counting With Deep Learning for Unbiased Stereology
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Solution Overview
Problem
Current stereology methods for quantifying cell number and size in tissue samples are time- and labor-intensive, requiring manual data collection and are not suitable for high-throughput applications, especially in samples with multiple cell types.
Innovation Solution
An automated stereology system using extended depth of field (EDF) images and deep learning models, such as convolutional neural networks (CNN), to accurately and efficiently count and size cells in tissue sections, employing Gaussian Mixture Models, morphological operations, and segmentation algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual stereology methods are used to quantify cell number and size, then measurement precision is maintained, but productivity is reduced due to time- and labor-intensive data collection
Solution Approach 1:
The patent replaces manual mechanical operations (manual focusing, manual object selection, manual counting) with an automated computer-based stereology system that uses digital imaging and algorithmic processing to perform the same measurements automatically, thereby increasing productivity while maintaining precision
Solution Approach 2:
The system performs self-service by automatically acquiring images, detecting objects of interest, measuring their properties, and generating quantitative results without requiring continuous human intervention for each measurement step, thus reducing time loss and increasing throughput
2Productivity
If automated stereology methods are implemented, then productivity is improved, but measurement precision may be compromised due to complexity of automation systems
Solution Approach 1:
The automated system incorporates feedback mechanisms where the software continuously processes images, detects objects, measures their properties, and validates results against predefined criteria, allowing for real-time correction of measurement errors and maintaining precision despite automation
Solution Approach 2:
The system creates digital copies of the physical tissue samples through high-resolution imaging, allowing multiple measurements and analyses to be performed on identical digital data without physical manipulation, thereby maintaining measurement precision while enabling automated high-throughput processing
3Productivity
If deep learning models are used for cell detection, then productivity is increased through rapid processing, but device complexity increases
Solution Approach 1:
The deep learning models are pre-trained on large datasets of annotated images before being deployed for actual cell counting, allowing the system to achieve high productivity through rapid inference while the complexity of model training and optimization is performed in advance during system development
Solution Approach 2:
The patent introduces an intermediary layer of software architecture that bridges the complex deep learning algorithms with the user interface and analysis workflow, abstracting the complexity away from the end user while maintaining high processing speed through optimized model inference
Data Source
AI summary
Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping/occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.