Microscopic Object Acquisition TRIZ Cases
- Microscope Image Area Selection Using ML-Based Magnification Guidance
ML-based image analysis highlights microscope regions worth enlarging, reducing operator burden while improving examination accuracy.
- Microscope Focus Repositioning Using ML Signatures for Thick Samples
ML image signatures let microscopes detect focus drift and restore target structures in thick, time-variable samples without added hardware.
- Lensless Optical Detection With LED Failure Compensation
Uniform LED current control keeps sensor detection values in range, limiting luminance errors and preserving lensless imaging accuracy after LED failure.
- Rock Thin Section Mineral And Grain Boundary Detection With One DNN
A single DNN combines optical, XPL, and electron microscopy data to identify minerals and grain boundaries faster and more consistently.
- Automated Slide Scanning Feedback for Targeted Image Rescans
User feedback is linked to slide identifiers and regions, enabling targeted scanner commands that correct poor image quality during rescanning.
- Whole-Slide Image ROI Detection Through Multi-Scale Nuclear Segmentation
See how multi-scale nuclear segmentation isolates hematoxylin-rich, eosin-poor regions to improve cancer focus identification.
- Time-Series Cell Image Analysis for Live-Dead Discrimination
Bright-field or phase-contrast image sequences track cell regions to improve training data for live-dead state discrimination.
- Time-Series Cell Image Analysis for Reliable Cell-State Labels
Time-series region tracking verifies cell candidates across images, improving label reliability for trained cell-state analysis.
- Whole Slide Image ROI Identification with Multi-Scale Nuclear Counting
Multi-scale nuclear counting, SURF, and RANSAC help identify and deduplicate cancer-focused ROIs across whole slide images.
- Automated Optical Training System for Microscope Sample Evaluation
Automated optical detection generates ground truth data for microscope evaluation, eliminating manual segmentation bottlenecks.
- Image Processing Device for Cell Type Differentiation
A processing unit rotates hue and adjusts saturation to maximize color distance between target pixels.
- Cell Classification via Membrane Blob Statistical Analysis
Color deconvolution and DoG filters extract faint membrane details to resolve nuclei detection reliability issues in complex tissue images.
- AI Imaging Flow Cytometry Cell Classification
Convolutional neural networks analyze multispectral images to classify cell types, reducing manual intervention while maintaining high objectivity.
- Microfluidic Cell Sorting via Impedance-Triggered Ejection Pauses
Impedance sensors trigger periodic ejection pauses for cell imaging, resolving the trade-off between throughput and separation accuracy.
- Automated Microscope Navigation for Sequential Slice Imaging
Coordinate transformation matrices map navigation images to microscope fields, resolving azimuth angle inconsistencies in sequential slice imaging.
- Microscope Image Synchronization via Timer-Mediated Association
A microscope apparatus associates image information from multiple acquisition methods using a timer for observation time.
- Biological Fluid Analyser Cell Classification via Multi-Plane Imaging
A biological fluid analyser obtains image data from multiple planes using distinct incident light settings to classify cells.
- Holographic Object Detection via Sparse Convolutional Dictionary Learning
Learned template dictionary mediates detection to resolve accuracy versus computational expense trade-offs in high-concentration samples.
- In Situ Biological Heterogeneity Characterization via Digital Tissue Segmentation
Digital tissue segmentation quantifies cellular features and biomarker expression levels within intact biological samples.