Object Count (General)
Object counting is a core image analysis task used across life science and materials research workflows. It is fundamental for quantifying structures, measuring distributions, and understanding experimental outcomes. The complexity of this process varies, depending on factors like contrast and object density.
Regardless of the challenge, the Image-Pro Object Count protocol enhances workflow efficiency. This protocol supports pre-trained deep learning models, machine learning, or threshold segmentation, enabling accurate counting even for the most difficult objects. It facilitates the analysis of large datasets, including complex formats and multiple image folders, all with minimal image analysis expertise required.
Techniques: Brightfield, Fluorescence, Photography, SEM, TEM
How it works
Select Channel
Select the channel that contains objects of interest.
Set Object Diameter
Set object diameter (for deep learning only).
Find Objects
Find objects with a pre-trained deep learning model, machine learning or threshold segmentation.
Quantitative results
Automatically generate tables, heat maps, charts and even complex bespoke reports.
Measurement parameters supported
- • Object Count
- • Total Object Area
- • Average Object Area
- • Average Diameter
- • Average Area
- • Custom user-defined measurements
Solution requirements
Required Modules
Base
2D Automated Analysis
Essentials Protocol Collection
Object Count Protocol
AI Deep Learning (Optional)
General Objects Model
Literature spotlight
- Arthur, D. E., Falke, J. A., Blain‐Roth, B. J., & Sutton, T. M. (2022). Alaskan yelloweye rockfish fecundity revealed through an automated egg count and digital imagery method. North American Journal of Fisheries Management, 42(4), 828-838.
- Dey, S., Lu, W., Haug, G., Chia, C., Larby, J., Weber, H. C., ... & Sohal, S. S. (2023). Airway inflammatory changes in the lungs of patients with asthma-COPD overlap (ACO): a bronchoscopy endobronchial biopsy study. Respiratory research, 24(1), 221
- Ying, H., Hang, Q., Cheng, G., Yang, S., Jin, J., Chen, Q., ... & Fang, M. (2021). The impact of the molecular pro le of the tumor microenvironment on the prognosis of NSCLC.
