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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 a single **NVIDIA RTX PRO 5000 (24 GB)** with a real whole-body CT (295 × 259 × 219, 2 mm). The batch size is **auto-selected from your free GPU VRAM**.
| Free VRAM | Batch (auto) | Peak VRAM | Time / case |
|:--|:--|:--|:--|
| 8 GB | 160 | ~7 GB | — |
| 16 GB | 320 | ~14 GB | — |
| 24 GB | 512 | ~10 GB | **~7 s** |
Single-model body segmentation keeps **system RAM ~1.6 GB**. The thin 2-D patches never fill the card, so inference stays compute-bound (~7 s, largely batch-independent). 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)
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