Automated Microscope Imaging for CNN Training Data
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Solution Overview
Problem
Current methods for training Convolutional Neural Networks (CNNs) in microscopic image analysis require extensive human effort for pre-classification and result in large, inefficient image files that consume significant storage and transmission resources.
Innovation Solution
A two-step imaging process combining low magnification pre-scan and candidate detection followed by high magnification capture, using automated scanning and optimized user interfaces for pre-classification, reduces the need for human intervention and minimizes data size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If whole slide images are scanned at target magnification to ensure complete coverage of biological material, then detection accuracy is improved, but scan time and data volume increase significantly
Solution Approach 1:
The scanning process is divided into two distinct stages: a low-magnification pre-scan stage for rapid overview and candidate detection, followed by a high-magnification targeted scan stage for detailed imaging. This segmentation allows the system to avoid scanning entire slides at high magnification, thereby reducing total scan time while maintaining detection accuracy for objects of interest.
Solution Approach 2:
A low-magnification pre-scan is performed before the high-magnification targeted scan to identify candidate regions containing objects of interest. This preliminary action enables the system to locate areas requiring detailed imaging in advance, avoiding unnecessary high-magnification scanning of empty or non-relevant areas, thus reducing overall scan time while preserving detection accuracy.
2Measurement precision
If whole slide images are scanned at target magnification to ensure complete coverage of biological material, then detection accuracy is improved, but data volume and storage requirements increase significantly
Solution Approach 1:
The system extracts only the relevant portions of the slide containing objects of interest by performing a low-magnification pre-scan to identify candidate regions, then scanning only those specific areas at high magnification. This extraction approach eliminates the need to store and process gigabytes of data from entire whole slide images, reducing data volume to only the essential regions containing biological material of interest.
3Measurement precision
If manual pre-classification is performed by human experts to provide training data for CNNs, then classification accuracy is improved, but workload and time consumption increase significantly
Solution Approach 1:
The system performs automatic pre-classification of detected objects using image analysis algorithms and machine learning models, eliminating the need for manual pre-classification by human experts. The system independently identifies, detects, and classifies objects of interest, generating training data automatically. This self-service capability maintains classification accuracy while dramatically reducing the workload and time consumption associated with manual expert annotation.
4Measurement precision
If high magnification is used for capturing images of objects of interest to ensure detailed visualization, then image quality is improved, but scan time and data volume increase
Solution Approach 1:
A low-magnification pre-scan is performed first to identify the precise locations of objects of interest, enabling subsequent high-magnification imaging to be targeted only at these specific regions. This preliminary detection step ensures that high magnification is applied only where necessary, maintaining image quality for objects of interest while minimizing the total area scanned at high magnification, thereby reducing scan time.
Data Source
Figure 1
AI summary
Methods are provided for efficient training of convoluted neural networks using computer-assisted microscope image acquisition and pre-classification of training images for biological objects of interest. The methods combine use of an automated scanning platform, on-the-fly image analysis parallel to the scanning process, and a user interface for review of the pre-classification performed by the software.