Microscope Image Conversion for Robust AI Segmentation
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Solution Overview
Problem
Existing microscopy systems face challenges in processing diverse microscope images robustly due to variations in image properties such as size, orientation, and brightness, leading to inaccurate segmentation and detection of samples, especially when the image resolution differs from the training data.
Innovation Solution
A microscopy system and method that convert microscope images to align their properties with those of the training data by resizing, rotating, or adjusting brightness to match the training images, using an image conversion program to enhance the reliability of image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the learned model is trained with training images showing structures with certain image properties, then the image processing accuracy is improved for those specific properties, but the system fails to robustly process microscope images with different image properties such as varying sizes, orientations, and brightness
Solution Approach 1:
The system dynamically changes image parameters (size, orientation, brightness) by applying geometric transformations and photometric adjustments to adapt the input microscope image to match the training data distribution, thereby resolving the contradiction between accuracy for specific properties and robustness to varying properties
Solution Approach 2:
The system performs preliminary image conversion before processing by the learned model, adjusting image properties in advance to align with training data characteristics. This preprocessing step ensures the learned model receives appropriately formatted input, maintaining high accuracy across diverse image conditions without requiring retraining
2Productivity
If the microscope image is processed directly by the learned model without conversion, then the processing speed is maintained, but segmentation and detection errors occur when image resolution differs from training data
Solution Approach 1:
The system performs a quick image conversion step before learned model processing to adjust image properties to match training data. This preliminary adjustment is computationally efficient and enables the learned model to process the converted image at full speed while achieving reliable segmentation and detection results
Solution Approach 2:
The image conversion program acts as an intermediary between the raw microscope image and the learned model. It transforms the input image into a format compatible with the learned model's training data distribution, enabling accurate processing without sacrificing speed
Data Source
AI summary
Processing a microscope image includes forming an input image from a microscope image before the input image is input into an image processing program. The image processing program comprises a learned model for image processing which is trained to calculate image processing results from input training images that show structures with certain image properties. The image processing program calculates an image processing result from the input image. The microscope image is converted into the input image by an image conversion program such that image properties of structures in the microscope image become closer to the image properties of the structures in the input training images.