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Compare biological models, docking engines, and computational workflows side-by-side. Filter by access model, compute footprint, and category to reach an actionable decision table in under a minute.
| Attribute | ＋ Add tool | |||
|---|---|---|---|---|
| Category | Research Imaging & Microscopy | Research Imaging & Structural Analysis | Research Imaging & Structural Analysis | — |
| Subcategory | Digital Pathology & Whole-Slide Analysis | Conventional or Statistical Baselines | Cell Segmentation | — |
| Company | University of Edinburgh | Research / method authors | Van Valen Lab | — |
| Access mode | Open Source | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | GPL-3.0 | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Standard desktop / workstation (multi-core CPU, optional GPU for deep learning) | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Oncology biomarker quantification, spatial histology analysis, and clinical trials | Not recorded | Not recorded | — |
| Research workflow | Whole-slide image -> cell segmentation -> phenotyping -> spatial biomarker scoring | Not recorded | Not recorded | — |
| Evidence level | Peer-reviewed research (Scientific Reports 2017) with thousands of citations | OFFICIAL_DOCUMENTATION | Peer-reviewed primary method | — |
| Commercial eligibility | Permissive open source release | NOT_INDEPENDENTLY_AUDITED | Unknown, requires audit | — |
| Price | Free Open Source | Verify access and licence | Check selected model licence | — |
| Availability ⓘ | Available for Windows, macOS, and Linux | Method paper or official documentation | Public research method/code | — |
| Features | High-performance whole-slide image viewing and annotation across gigapixel files, Machine learning tissue classification (tumor vs stroma vs necrosis), Integration with StarDist and Cellpose for deep learning cell and nuclear segmentation, Extensive scripting API in Groovy and Python for automated cohort analysis | CellProfiler scientific method, Microscopy images and configured classical image-analysis modules, Segmented objects and quantitative image features | Automated cell instance segmentation using detector and Segment Anything models for supported microscopy., Individual cell instance masks | — |
| Entry type | Open Source Software | Classical/statistical scientific method | Research AI model / method | — |
| AI role | Deep Learning Image Segmentation | NON_AI_CLASSICAL | Published biomedical AI method | — |
| Input data | Not recorded | Microscopy images and configured classical image-analysis modules | Microscopy images; chosen checkpoint and pixel size | — |
| Output data | Not recorded | Segmented objects and quantitative image features | Individual cell instance masks | — |
| Validation | Not recorded | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | Digital pathology bioimage analysis platform | Not recorded | Not recorded | — |
| Intended use | Not recorded | Not recorded | Not recorded | — |
| Compatibility | Not recorded | Not recorded | Not recorded | — |
| Manufacturer | University of Edinburgh (Bankhead Lab) | Not recorded | Not recorded | — |
| Integration evidence | https://qupath.github.io | Not recorded | Not recorded | — |
| Laboratory handoff | Correlates histological features with genomic data and patient survival curves | Not recorded | Not recorded | — |