Overview
We turn images into measurements: segmenting cells, tracking structures and quantifying signal.
Salient features
- Deep-learning cell segmentation
- Batch pipelines for large image sets
- Quantitative, exportable measurements
Analyses we offer
SPIM
Registers, fuses and deconvolves light-sheet microscopy volumes.
Methods: BigStitcher, SPIM_Registration, pycudadecon.
Whole slide imaging
Analyses gigapixel pathology slides for tissue and cell features.
Methods: qupath, openslide, CLAM.
Image Processing
Builds reproducible image-analysis pipelines for filtering, segmentation and measurement.
Methods: scikit-image, fiji, CellProfiler.
Super-resolution imaging
Reconstructs and analyses single-molecule localisation microscopy data.
Methods: thunderstorm, SMAP, ZeroCostDL4Mic.
Bright-field microscopy analysis
Segments and counts cells in label-free images.
Methods: cellpose, stardist, CellProfiler.
Tissue array analysis
Quantifies markers across tissue microarray cores and multiplexed images.
Methods: qupath, mcmicro.
Scanning force microscopy
Analyses AFM topography and force data of biomolecules and surfaces.
Methods: TopoStats, PyJibe.
Cell segmentation & bioimage viewer
Segments cells with deep learning and provides interactive review of the results.
Methods: cellpose, napari.
What you receive
- A written report with methods, parameters and interpretation
- Processed data tables and publication-quality figures
- Scripts and settings so every result can be reproduced
Not sure which analysis fits your data? Contact us and we will advise.