AI-Based Microscopy Image Analysis
Overview
This service provides AI-supported analysis of microscopy images for laboratory, biomedical, pharmaceutical, and materials-science workflows. It is designed for quantitative image analysis tasks such as detecting, counting, and characterizing cells, particles, or other microscopic structures in large image collections. The service includes preparation of representative image datasets, definition of annotation or reference protocols, model training or adaptation for the target imaging modality, and delivery of reproducible analysis workflows. It helps users replace slow and variable manual inspection with standardized image-based measurements that support research, development, and routine laboratory processing. The service uses deep-learning-based detection and localization methods for microscopy image analysis, including cell detection and counting and point localization with morphology characterization for nanoparticle analysis in electron microscope images. The main methodology used is CeDiRNet for nanoparticle detection and characterization, while the cell-analysis workflow is based on trained microscopy detection and localization models adapted to the target imaging setup and dataset.
Outcome
Deliverables include a prepared training/adaptation workflow, fine-tuned model(s), and a batch inference pipeline for microscopy images. Outputs can include detections, counts, point locations, confidence scores, derived characteristics such as size or shape, and visual overlays or summary tables for review.
Service Card
| Category | AI development and deployment |
| Domain | Microscopy Image Analysis, Materials Science |
| Vertical | Health and Biotechnology |
| Target users | Biomedical Researchers, Healthcare and Biotech R&D Teams, Pharma Laboratories, Scientific Institutes, Materials Science R&D Groups |
| Service type | Model and Pipeline |
| Outputs | Training/adaptation workflow, fine-tuned model(s), and inference results including detections, counts, point locations, confidence scores, derived characteristics, and review visualisations. |
| Requirements | Requires NVIDIA GPU node. Annotated microscopy or SEM images representative of the target workflow for training; annotations may include object locations, counts, and optional attributes such as size or shape. |
| Limitations | |
| Availability | Q3/2027 |
| Licensing | CC BY-NC-SA 4.0 |
| Visual content | |
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