ImpactSeg — Multimodal Body Segmentation
Multimodal (CBCT / MR / CT) anatomical body segmentation (11 structures), built with 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
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 — 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 measured on your GPU (a forward of one patch, then of two, then the batch the free VRAM holds).
| 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
- 📦 PyPI: impact_seg_konfai
- 📄 Paper: arXiv:2510.21358