Potestates/DeepSparse-40v

DeepSparse — 40-view finetuned checkpoints

Finetuned DeepSparse checkpoints for 40-view (180°) sparse-view CT on four anatomies, from the official pretrained weights (pretrain/ep_700.pth) with the official two-stage protocol. Companion to Potestates/DeepSparse-25v.

Results (official test splits, 256³)

Checkpoint	Train cases	Test cases	3D PSNR (dB)	3D SSIM (×10⁻²)	2D PSNR (dB)	2D SSIM
pelvis+40v+n250+nv60+s2 (PENGWIN)	60 (all)	30	31.54	92.02	39.41	0.9842
luna+40v+n250+nv60+s2 (LUNA16)	250 / 738	100	32.96	92.71	40.18	0.9871
abdomen+40v+n250+nv60+s2 (PANORAMA)	250 / 1244	600	evaluation running
tooth+40v+n250+nv60+s2 (ToothFairy)	250 / 343	75	evaluation running

3D metrics from the official code/evaluate.py (skimage 3D SSIM, 7³ window). 2D metrics reproject both volumes with TIGRE and score them with R²-Gaussian's metric_proj (100 random views over 360°, each view normalized by its own max). Per-case values are in each folder's results_1.0x.csv / results_proj2d_1.0x.csv.

25 views → 40 views

Dataset	3D PSNR 25v	3D PSNR 40v	Δ
Pelvis	30.96	31.54	+0.58
Lung	32.47	32.96	+0.49
Abdomen	29.79	pending
Tooth	34.03	pending

Stage-1 validation PSNR (half resolution, epoch 400) already favours 40 views for the two pending datasets: tooth 34.38 → 35.18, abdomen 30.54 → 30.65.

Setup

• Code: xmed-lab/DeepSparse @ a055aba3bcb5732a68f89cc0bf3ee6fbfbb1b1e4
• Views: 40 input views, uniform over 180°, taken from a 600-view projection cache (600/40 = 15, so the views are exactly equispaced; the 300-view cache used for 25v cannot divide 40 evenly)
• Stage 1: --num_views 60 --min_views 40 --random_views --vq_w 0.1, resumed from pretrain/ep_700.pth, 400 epochs
• Stage 2: same views, --vq_w 1.0 --safely_load --freeze_ft, resumed from stage-1 ep_400.pth, 400 epochs
• num_views=60 gives a 1.5× teacher/student view ratio in stage 2 (official: 24 dense views vs 6/8/10)
• Batch size 2, lr 1e-4, weight decay 1e-3, no LR schedule, 1 GPU per job
• Environment: PyTorch 2.7.1 + CUDA 12.8, TIGRE 3.1.3

The 600-view cache was rebuilt from raw data with the official preprocessing; for every case the recomputed image is bit-identical to the existing processed volume, and the 300 shared views agree with the old cache at 53–55 dB (uint8 storage rounding).

Differences from the paper protocol

1. Training set size: at most 250 training cases per dataset (the paper uses the full training sets). Test splits are the official ones; case lists in splits/<DATASET>/meta_info.json.
2. Dense views: num_views=60 (1.5×), against 2.4–4× in the official 6/8/10-view settings.
3. Environment: newer PyTorch and TIGRE than the official (1.13 / 2.3), required for Blackwell GPUs.
4. ToothFairy config: the official meta_info.json points at an unreleased config+new.yaml / processed+new/; the repo's public config.yaml is used instead (verified with the official tooth 10-view checkpoint: 31.89 dB vs the paper's 31.79).

Files

<dataset>+40v+n250+nv60+s2/
  ep_400.pth                   final stage-2 checkpoint (use this)
  config.yaml                  config saved by train.py (min_views stays 10 here)
  results_1.0x.csv             per-case 3D PSNR/SSIM
  results_proj2d_1.0x.csv      per-case 3D + 2D projection metrics
  train.log / train_s1.log     stage-2 / stage-1 logs (args + validation curve)
configs/                       training configs and the 40-view eval config (min_views: 40)
splits/<DATASET>/meta_info.json  the 250-case subset and official test split

Usage

hf download Potestates/DeepSparse-40v --local-dir ./logs
cp logs/configs/eval_40v_n250_600v.yaml configs/generated/   # set root_dir to your data root

python code/evaluate.py --name luna+40v+n250+nv60+s2 --epoch 400 --dst_name luna \
  --split test --num_views 40 --cfg_path configs/generated/eval_40v_n250_600v.yaml \
  --out_res_scale 1.0

Evaluation must use min_views: 40 and a projection cache whose view count is a multiple of 40 (we use 600); the config.yaml inside each checkpoint folder still says min_views: 10.

License

DeepSparse code is MIT. These weights derive from datasets with their own terms: PANORAMA (abdomen) CC BY-NC 4.0 (non-commercial), PENGWIN (pelvis) CC BY 4.0, LUNA16 and ToothFairy per their original terms. Please cite the DeepSparse paper and the datasets.