Microscopy Image Sectioning for Parallel Processing
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Solution Overview
Problem
Long processing times for microscopy images hinder timely responses in experiments and affect the throughput of automated systems like high content screening, as existing methods struggle to efficiently identify and measure objects in these images.
Innovation Solution
A computer-implemented method and system that divide microscopy images into sections, which are then processed in parallel by multiple units, reducing processing time through efficient object detection, segmentation, and measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional sequential processing methods are used to identify and measure objects in microscopy images, then processing accuracy can be maintained, but processing time becomes excessively long
Solution Approach 1:
The microscopy image is divided into multiple smaller sections or tiles, allowing parallel processing of each section by different processing units. This segmentation reduces the computational burden on individual processors and enables simultaneous analysis of multiple image regions, thereby significantly reducing overall processing time while maintaining object detection accuracy through proper handling of objects that span section boundaries.
2Productivity
If processing speed is increased through parallel processing, then throughput improves, but system complexity increases
Solution Approach 1:
The image is segmented into multiple sections that can be independently processed by parallel computing units such as GPU threads or multi-core processors. This segmentation enables throughput improvement through parallel processing while managing system complexity by breaking down the complex task into simpler, independent sub-tasks that can be handled by standard parallel computing architectures.
Solution Approach 2:
An intermediary data structure or buffer system is introduced to manage the distribution of image sections to processing units and the aggregation of results. This intermediary layer simplifies the coordination complexity by providing a standardized interface between the parallel processing units and the main control system, making the overall system easier to manage despite the increased parallelism.
3Measurement precision
If the entire microscopy image is processed as a single unit, then object detection accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The image is divided into sections with overlapping regions or extended boundary zones. Objects that span across section boundaries are detected in multiple sections and then merged or deduplicated. This approach maintains object detection accuracy by ensuring complete object coverage while reducing processing time through parallel processing of smaller sections.
4Productivity
If parallel processing is implemented to reduce processing time, then throughput increases, but coordination and data integration become more complex
Solution Approach 1:
An intermediary data structure or buffer system is introduced to manage the distribution of image sections to processing units and the aggregation of results. This intermediary layer simplifies the coordination complexity by providing a standardized interface between the parallel processing units and the main control system, making the overall system easier to manage despite the increased parallelism.
Solution Approach 2:
The system implements feedback mechanisms where processing results from each section are continuously monitored and integrated. This feedback approach allows for real-time coordination of parallel processing operations, enabling dynamic load balancing and error handling while maintaining throughput efficiency.
Data Source
AI summary
A computer-implemented system and method of processing a microscopy image are provided. A microscopy image is received, and a configuration for an image section that includes a portion of the microscopy image is determined. Multiple image sections are respectively assigned to multiple processing units, and the processing units respectively process the image sections in parallel. One or more objects are determined to be respectively present in the image sections, and the objects present in the image sections are measured to obtain object data associated with the objects.