| license: apache-2.0 | |
| library_name: konfai | |
| pipeline_tag: image-segmentation | |
| tags: | |
| - medical-imaging | |
| - segmentation | |
| - multimodal | |
| - ct | |
| - mri | |
| - cbct | |
| - konfai | |
| # ImpactSeg — Multimodal Body Segmentation | |
| Multimodal (**CBCT / MR / CT**) anatomical **body segmentation** (11 structures), built with | |
| [**KonfAI**](https://github.com/vboussot/KonfAI). One model handles all three modalities. | |
| ## 🧩 Model | |
| | Model | Input | Output | Labels | Ensemble | | |
| |:--|:--|:--|:--:|:--:| | |
| | `body` | Volume (CT / MR / CBCT) | Segmentation | 11 | 1 | | |
| 2.5D residual-encoder UNet · patch `[1, 192, 192]` · resampled to 3 mm. | |
| ## 🚀 Usage | |
| ```bash | |
| pip install impact_seg_konfai | |
| impact-seg-konfai segment body -i input.nii.gz -o output/ | |
| ``` | |
| - **Generic runner:** `konfai-apps infer VBoussot/ImpactSeg:body -i input.nii.gz -o output/` | |
| - **Interactive:** [**SlicerKonfAI**](https://github.com/vboussot/SlicerKonfAI) — the ⚙ *Advanced* dialog overrides patch size and batch size. | |
| ## ⚡ Performance & VRAM | |
| Benchmarked on an **NVIDIA RTX PRO 5000 (24 GB)**. The batch size is **auto-selected from your free GPU VRAM**. | |
| | Free VRAM | Batch (auto) | Peak VRAM | | |
| |:--|:--|:--| | |
| | 8 GB | 160 | ~7 GB | | |
| | 16 GB | 320 | ~14 GB | | |
| | 24 GB | 512 | ~22 GB | | |
| ≈ **16 s / case** on the benchmark volume (scales with case size). Override with `--patch-size` / `--batch-size`. | |
| ## 🔗 Links | |
| - 🧠 **KonfAI:** [github.com/vboussot/KonfAI](https://github.com/vboussot/KonfAI) | |
| - 📦 **PyPI:** [impact_seg_konfai](https://pypi.org/project/impact_seg_konfai/) | |
| - 📄 **Paper:** [arXiv:2510.21358](https://arxiv.org/abs/2510.21358) | |