Adaptive Specimen Imaging With ANN-Guided ROI Scanning
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Solution Overview
Problem
The existing methods for acquiring high-quality specimen images in microscopy are time-consuming and resource-intensive, as they require scanning specimens at high-quality settings, which can be inefficient and potentially damaging to certain sample types.
Innovation Solution
An adaptive specimen image acquisition method using an artificial neural network to identify regions of interest, allowing for initial low-quality scanning, generation of a scan mask to focus high-quality scanning only on these regions, and merging low-quality and high-quality images to produce a final image with high-quality objects of interest and lower-quality background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-quality microscopy parameters are used to scan the entire specimen, then image quality is improved, but time and resource consumption increase significantly
Solution Approach 1:
The specimen scanning process is divided into two segments: a first low-quality scan of the entire specimen to identify regions of interest, and a second high-quality scan focused only on those identified regions. This segmentation allows the system to apply high-quality parameters selectively rather than uniformly across the entire specimen, thereby reducing total scanning time while maintaining image quality where it matters most.
Solution Approach 2:
The system applies different quality levels to different regions of the specimen based on their importance. Regions identified as containing objects of interest receive high-quality scanning parameters, while other regions receive low-quality scanning. This local quality approach ensures that computational resources and time are concentrated on areas that matter most for analysis.
2Measurement precision
If high-quality microscopy parameters are used, then image quality is improved, but energy consumption increases
Solution Approach 1:
The scanning process is segmented into a preliminary low-energy phase (first scan at low quality) and a targeted high-energy phase (second scan at high quality only for regions of interest). This segmentation reduces total energy consumption by limiting high-energy operations to only the necessary portions of the specimen.
Solution Approach 2:
The system performs a partial high-quality scan rather than a complete high-quality scan of the entire specimen. By identifying and scanning only the regions containing objects of interest at high quality, the system avoids excessive energy consumption that would result from uniformly applying high-quality parameters across the entire specimen area.
3Measurement precision
If the entire specimen is scanned at high quality, then complete image quality is achieved, but productivity decreases
Solution Approach 1:
A preliminary low-quality scan is performed first to identify regions of interest before conducting the high-quality scan. This preliminary action enables the system to plan the subsequent high-quality scanning strategy, ensuring that time-consuming high-quality scanning is applied only where necessary, thereby improving overall productivity.
Solution Approach 2:
The scanning workflow is segmented into an initial survey phase and a detailed examination phase. This segmentation allows the system to quickly identify target areas and then focus resources on those areas, significantly improving scanning efficiency compared to uniformly scanning the entire specimen at high quality from the start.
Data Source
AI summary
Techniques for adapting an adaptive specimen image acquisition system using an artificial neural network (ANN) are disclosed. An adaptive specimen image acquisition system is configurable to scan a specimen to produce images of varying qualities. An adaptive specimen image acquisition system first scans a specimen to produce a low-quality image. An ANN identifies objects of interest within the specimen image. A scan mask indicates regions of the image corresponding to the objects of interest. The adaptive specimen image acquisition system scans only the regions of the image corresponding to the objects of interest, as indicated by the scan mask, to produce a high-quality image. The low-quality image and the high-quality image are merged in a final image. The final image shows the objects of interest at a higher quality, and the rest of the specimen at a lower quality.