metadata
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. 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 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
- 📦 PyPI: impact_seg_konfai
- 📄 Paper: arXiv:2510.21358