TotalSegmentator-KonfAI

KonfAI-accelerated adaptation of TotalSegmentator — whole-body multi-organ CT / MRI segmentation, built with KonfAI.

🧩 Models

Task	Modality	Labels	Ensemble	Notes
total	CT	117	5	full accuracy
total-3mm	CT	117	1	fast (3 mm)
total_mr	MRI	50	2
total_mr-3mm	MRI	50	1	fast (3 mm)

3D residual UNet · patch [96, 128, 160] · resampled to 1.5 mm.

🚀 Usage

pip install totalsegmentator-konfai
totalsegmentator-konfai segment total -i input_ct.nii.gz -o output/

• Generic runner: konfai-apps infer VBoussot/TotalSegmentator-KonfAI:total -i input_ct.nii.gz -o output/
• Interactive: SlicerKonfAI — the ⚙ Advanced dialog overrides patch size and batch size.

⚡ Performance & VRAM

Same input, same weights (Datasets 291–295, 1.5 mm, 5-model total), same PyTorch build (2.12.1, cu13.0), single NVIDIA RTX PRO 5000 (24 GB), TotalSegmentator 2.18.0. Peak RAM = process-tree resident set; peak VRAM = over baseline. Measured with KonfAI's benchmarks/perf/bench_apps.py (2026-09-09).

Case (voxels)	Tool	Time	Peak RAM	Peak VRAM
S (240 × 220 × 200)	KonfAI	7.6 s	5.0 GB	12.3 GB
	Original	29.9 s	22.6 GB	3.3 GB
M (249 × 246 × 246)	KonfAI	17.7 s	5.2 GB	15.4 GB
	Original	58.3 s	25.2 GB	7.1 GB
L (512 × 512 × 531)	KonfAI	212 s	17.6 GB	10.6 GB
	Original	377 s	47.9 GB	23.1 GB

1.8–3.9× faster, 2.7–4.8× less host RAM. KonfAI trades more VRAM on small/medium cases (larger patches, GPU accumulation) for the speed-up while staying inside 24 GB; on large cases streaming bounds VRAM (10.6 GB) where the original nears the card limit (23.1 GB), so KonfAI is then lighter on both RAM and VRAM. The batch size is auto-selected from your free VRAM; on cards below 24 GB use total-3mm (1 model, 3 mm). Override with --patch-size / --batch-size.

🔗 Links

• 🧠 Original TotalSegmentator: github.com/wasserth/TotalSegmentator
• 🧠 KonfAI: github.com/vboussot/KonfAI
• 📦 PyPI: totalsegmentator-konfai