Edit Guided Processing for Time-Lapse Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current time-lapse image analysis in bioscience is hindered by the complexity of manual or semi-automatic processing required for mask detection and object tracking, leading to inefficiencies and errors that accumulate over time, especially in high-volume data scenarios.
Innovation Solution
A computerized edit guided processing framework that assists in efficient mask and track editing, allowing users to improve processing recipes and parameters without image processing knowledge, and logs processing updates for archiving and future reference, using assisted editing tools and guided processing methods to enhance automatic detection and tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual or semi-automatic processing is used for mask detection and object tracking, then processing flexibility and adaptability are maintained, but user effort and time consumption increase significantly
Solution Approach 1:
The patent introduces an edit-guided processing framework that acts as an intermediary between manual editing and fully automatic processing. Users provide edit guidance (annotations, corrections, or preferences) which the system then uses to automatically generate processing results, reducing direct manual effort while maintaining adaptability through the guidance mechanism.
Solution Approach 2:
The system performs self-service by automatically learning from user edits and improving its processing algorithms without requiring users to have image processing expertise. The framework uses edit guidance to self-adjust parameters and improve detection/tracking performance autonomously across the image sequence.
2Reliability
If basic manual editing tools are used, then simplicity is maintained, but errors accumulate over time in time-lapse sequences
Solution Approach 1:
The system performs preliminary actions by pre-processing the image sequence and pre-generating masks and tracks before user review. This allows errors to be detected and corrected systematically rather than accumulating, as the framework prepares all processing results in advance for efficient user validation and correction.
Solution Approach 2:
The edit-guided framework implements feedback mechanisms where user corrections and edits are fed back into the processing algorithm. The system learns from these corrections and adjusts its parameters accordingly, preventing error accumulation by continuously improving based on user feedback throughout the processing of the time-lapse sequence.
3Reliability
If users review and correct mistakes in early frames, then tracking accuracy improves, but significant time is lost due to repeated corrections across all frames
Solution Approach 1:
The system generates preliminary tracking results for all frames before user review, allowing users to make single corrections that automatically propagate forward. This preliminary generation approach prevents the need to repeatedly review and correct the same errors across multiple frames, as corrections are applied systematically to the entire sequence.
Solution Approach 2:
The framework segments the correction process by allowing users to focus on specific problematic regions or frames rather than reviewing the entire sequence. The edit guidance can be applied selectively to particular areas, and the system automatically extends these corrections appropriately through the time-lapse sequence, reducing overall review time.
4Measurement precision
If automatic processing is used without user guidance, then productivity is high, but detection and tracking precision deteriorate due to lack of domain knowledge
Solution Approach 1:
The edit-guided framework serves as an intermediary that translates user domain knowledge into processing parameters without requiring users to understand image processing techniques. Users provide high-level guidance (edits, corrections, preferences) and the system automatically converts these into precise detection and tracking parameters, maintaining both precision and productivity.
Solution Approach 2:
The system automatically adjusts processing parameters based on user edit guidance without requiring users to manually configure technical parameters. The framework learns from edits and dynamically changes detection and tracking parameters to optimize precision while maintaining high processing efficiency through automated parameter optimization.
Data Source
AI summary
A computerized mask edit guided processing method for time-lapse image analysis performs by a computer program an assisted mask editing on an input image sequence to generate mask edit data, and performs a mask edit guided processing using the image sequence and the mask edit data. A computerized track edit guided processing method for time-lapse image analysis performs by a computer program an assisted track editing on an input image sequence to generate track edit data, and performs a track edit guided processing using the image sequence and the track edit data. A computerized edit guided processing method for time-lapse image analysis performs by a computer program a combination of assisted mask editing and assisted track editing on an input image sequence to generate edit data, and performs a combination of mask edit guided processing and track edit guided processing using the image sequence and the edit data.