---
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)