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 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
• 📦 PyPI: impact_seg_konfai
• 📄 Paper: arXiv:2510.21358