Hidden

Cryo-Electron Tomography (Cryo-ET) — Hidden Tier

(3 scenes)

Fully blind server-side evaluation — no data download.

What you get

No data downloadable. Algorithm runs server-side on hidden measurements.

How to use

Package algorithm as Docker container / Python script. Submit via link.

What to submit

Containerized algorithm accepting y + H, outputting x_hat + corrected spec.

Parameter Specifications

🔒

True spec hidden — blind evaluation, only ranges available.

Parameter Spec Range Unit
tilt_axis_offset -0.42 – 1.38 px
tilt_angle_accuracy -0.14 – 0.46 degpertilt
dose_induced_shrinkage -1.4 – 4.6 -
ctf_per_tilt_variation -0.15 – 0.15 um
missing_wedge 27.2 – 39.2 deg

Hidden Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 ETFormer + gradient 0.619 24.64 0.791 0.78 ✓ Certified Chen et al., CVPR 2024
2 DiffusionET + gradient 0.618 24.1 0.772 0.84 ✓ Certified Zhang et al., arXiv 2024
3 DeePiCt + gradient 0.612 24.36 0.781 0.78 ✓ Certified Moebel et al., Nat. Methods 2021
4 DeepDeWedge + gradient 0.607 23.34 0.744 0.88 ✓ Certified Wiedemann et al., Nat. Methods 2024
5 IMOD + gradient 0.575 22.78 0.722 0.79 ✓ Certified Kremer et al., J. Struct. Biol. 1996
6 CryoSeg + gradient 0.515 20.62 0.628 0.79 ✓ Certified Lamm et al., Nat. Methods 2022
7 SART-ET + gradient 0.506 19.88 0.593 0.85 ✓ Certified Andersen & Kak, Ultrason. Imaging 1984
8 IsoNet + gradient 0.454 18.06 0.503 0.86 ✓ Certified Liu et al., Nat. Commun. 2021
9 WBP + gradient 0.429 17.23 0.462 0.86 ✓ Certified Crowther et al., Proc. R. Soc. 1970

Dataset

Scenes: 3

Scoring Formula

0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)

PSNR: 40% SSIM: 40% Consistency: 20%
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