• Simple or complex – create any analysis protocol you require for the assay of interest. Segment and extract measurements from any number of channels
• Tailored – define target hierarchy in the protocol via target linking. Use Custom Measure option to calculate additional measurements for any of the biological targets
• Compelling – leverage deep learning segmentation option at any of the analysis steps. Target masks can be combined using logical operations
• Easy to use – intuitive user interface with dynamic panes enables focused protocol development
• Help is just a click away – easily access contextual help with an interactive, searchable user guide

• Accurate – deep learning can maintain accuracy across difficult to segment samples including confluent cells, low signal-to-noise samples and transmitted light images
• Reliable – SINAP models can account for high phenotypic variability
• Flexible – a single workflow can deal with a variety of applications and imaging modalities
• Accessible – trained model learns to segment from scientist’s drawing on the image rather than asking a deep-learning guru to create a new model and optimize multiple parameters

Classification of spheroids formed from HCT116 cells. Spheroids were segmented based on brightfield images using SINAP. Sample was additionally counter-stained with with Hoechst 33342, Calcein AM and MitoTracker Red to visualize nuclei, live cells and mitochondria respectively.

• Comprehensive – a data driven approach that starts with an unsupervised clustering to find patterns in the data and highlight subpopulations without prior knowledge of what phenotypes may exist.
• Robust – dedicated machine learning algorithm identifies the optimal set of descriptive features to avoid overfitting of the resulting classification model.
• Optimized workflow – classification is achieved by simply confirming or correcting the algorithm’s predictions until it learns the right behavior.

• Create simple step-by-step custom analysis
• Tailor object segmentation and classification
• Find objects localized within defined biological compartments
• Report only measurements required for an assay of interest
• Analyze live imaging data
• True 3D segmentation (3D application only)
• Robust reconstruction of 2D segmentation into volume for 3D assays (3D application only)

Example segmentation of HCT116 cells forming a spheroid segmented in Custom Module Editor 3D and visualized in 3D viewer