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Comparez modèles biologiques, moteurs de docking et flux de calcul côte à côte. Filtrez par modèle d'accès, empreinte de calcul et catégorie pour parvenir à une table de décision exploitable en moins d'une minute.
| Attribut | ＋ Ajouter un outil | |||
|---|---|---|---|---|
| Category | Research Imaging & Microscopy | Research Imaging & Structural Analysis | Research Imaging & Structural Analysis | — |
| Subcategory | Multi-Dimensional Bioimage Visualization | Conventional or Statistical Baselines | Cell Segmentation | — |
| Company | Chan Zuckerberg Initiative (CZI) & Community | Research / method authors | Van Valen Lab | — |
| Access mode | Open Source | Confirm distribution and exact release | Research code or publication | — |
| Licence conditions | BSD-3-Clause | NOT_INDEPENDENTLY_AUDITED | Check code and model weights separately | — |
| Compute requirements | Standard workstation with OpenGL 3.3+ GPU support | Version and data dependent | Check selected checkpoint and runtime; no GPU catalog recommendation | — |
| Biological application | Fluorescence microscopy, lightsheet imaging, electron microscopy, and spatial biology | Non enregistré | Non enregistré | — |
| Research workflow | Load imaging data -> interactive visual inspection -> apply segmentation plugins -> export measurements | Non enregistré | Non enregistré | — |
| Evidence level | Standard viewer endorsed by the global bioimage analysis community | 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 on PyPI and Conda | Method paper or official documentation | Public research method/code | — |
| Features | High-performance rendering of multi-gigabyte 3D and 4D image stacks powered by VisPy, Rich layer system (Images, Points, Shapes, Labels, Tracks, Vectors, Surfaces), Thriving plugin ecosystem integrating Cellpose, StarDist, and deep learning tools, Native integration with NumPy, SciPy, and PyTorch tensors | 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 | Multi-Dimensional Image Visualization | NON_AI_CLASSICAL | Published biomedical AI method | — |
| Input data | Non enregistré | Microscopy images and configured classical image-analysis modules | Microscopy images; chosen checkpoint and pixel size | — |
| Output data | Non enregistré | Segmented objects and quantitative image features | Individual cell instance masks | — |
| Validation | Non enregistré | Published method scope; no independent BioAtlas execution or benchmark | Requires independent dataset-specific evaluation | — |
| Type | Interactive bioimage viewer and analysis ecosystem | Non enregistré | Non enregistré | — |
| Intended use | Non enregistré | Non enregistré | Non enregistré | — |
| Compatibility | Non enregistré | Non enregistré | Non enregistré | — |
| Manufacturer | Napari Developers & Chan Zuckerberg Initiative | Non enregistré | Non enregistré | — |
| Integration evidence | https://napari.org | Non enregistré | Non enregistré | — |
| Laboratory handoff | Enables interactive quality control of automated segmentation results | Non enregistré | Non enregistré | — |