Automated Cell Region Selection via Axis Detection
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Solution Overview
Problem
Manual selection of regions of interest in biological cell images is time-consuming and inefficient, hindering the accuracy and reliability of experiments like FRAP and FLIP.
Innovation Solution
A method that automatically identifies regions of interest by detecting primary and secondary axes of a biological cell, dividing the cytoplasm into areas, determining geometric features, and selecting areas based on predefined criteria, using an analysis unit in a microscope system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of regions of interest is performed, then selection accuracy can be maintained, but time consumption increases and efficiency decreases
Solution Approach 1:
The system performs self-service by automatically detecting cell structures, calculating axes, dividing cytoplasm into areas, and selecting regions of interest without human intervention. The analysis unit autonomously completes the entire ROI selection process that previously required manual operator input, thereby eliminating time consumption while maintaining selection accuracy through algorithmic precision.
Solution Approach 2:
The manual mechanical process of visually inspecting and selecting ROIs is replaced by an automated analysis unit that uses image processing algorithms. The system substitutes human visual inspection and manual selection with computational methods including axis detection, cytoplasm division, and automated ROI identification based on geometric features and predefined criteria.
2Productivity
If automated region selection is implemented, then efficiency and productivity increase, but system complexity increases
Solution Approach 1:
The automated selection process is segmented into distinct functional modules: image acquisition, cell structure detection, primary axis detection, secondary axis detection, cytoplasm division into areas, geometric feature calculation, and ROI selection based on predefined criteria. This modular segmentation manages system complexity by organizing functions into discrete, manageable components within the analysis unit.
Solution Approach 2:
The analysis unit serves multiple functions: it detects cell structures, calculates axes, divides cytoplasm, analyzes geometric features, and selects ROIs based on various predefined criteria. This multi-functionality consolidates what would otherwise require multiple separate tools or manual operations into a single automated system, increasing productivity while managing complexity through integration.
3Reliability
If automated analysis with predefined criteria is used, then objectivity and reliability improve, but adaptability to different cell types may decrease
Solution Approach 1:
The system maintains reliability through predefined selection criteria while achieving adaptability by allowing modification of these criteria parameters. The analysis unit can adjust geometric feature thresholds, area selection rules, and ROI definition parameters to suit different cell types and experimental requirements, ensuring both objective consistency and flexibility across diverse biological samples.
Data Source
Figure 1A
Figure 1B~1C
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AI summary
Techniques for identifying a region of interest in an image of a biological cell include detecting a primary axis of a structure of the biological cell and defining a first section line along the detected primary axis, detecting at least one secondary axis of the structure of the biological cell and defining a second section line along each of the at least one secondary axes. Based on the second section line along each of the at least one secondary axes and the first section line along the detected primary axis, a cytoplasm of the cell is divided into a plurality of areas. A geometric feature of each of the areas is determined and one of the areas is selected according to predefined criteria. The image data of the selected area is analyzed, and the image data is compared with the predefined criteria to identify regions that comply with the provided criteria. At least one area complying with the predefined criteria to a pre-defined degree is selected as the region of interest.