Dev

Correlative Light-Electron Microscopy (CLEM) — Dev Tier

(3 scenes)

Blind evaluation tier — no ground truth available.

What you get

Measurements (y), ideal forward operator (H), and spec ranges only.

How to use

Apply your pipeline from the Public tier. Use consistency as self-check.

What to submit

Reconstructed signals and corrected spec. Scored server-side.

Parameter Specifications

🔒

True spec hidden — estimate parameters from spec ranges below.

Parameter Spec Range Unit
registration_error_(lm_to_em) -120.0 – 180.0 nm
sample_deformation_(fixation) -1.2 – 1.8 shrinkage
fluorescence_preservation 74.8 – 116.8 -

Dev Tier Leaderboard

# Method Score PSNR SSIM Consistency Trust Source
1 SwinCLEM + gradient 0.775 31.89 0.941 0.9 ✓ Certified Huang et al., IEEE TMI 2023
2 DiffusionCLEM + gradient 0.765 31.81 0.941 0.85 ✓ Certified Chen et al., Nat. Methods 2024
3 TransMorph + gradient 0.733 29.52 0.909 0.87 ✓ Certified Chen et al., Med. Image Anal. 2022
4 CLEM-Net + gradient 0.704 27.66 0.873 0.89 ✓ Certified Spiers et al., Nat. Methods 2021
5 VoxelMorph + gradient 0.691 27.5 0.87 0.84 ✓ Certified Balakrishnan et al., IEEE TPAMI 2019
6 PINN-CLEM + gradient 0.678 26.94 0.857 0.83 ✓ Certified Löffler et al., Nat. Methods 2023
7 Landmark-Reg + gradient 0.590 23.44 0.748 0.78 ✓ Certified Arganda-Carreras et al., Bioinformatics 2006
8 CNN-Reg + gradient 0.579 22.25 0.701 0.88 ✓ Certified de Vos et al., NeuroImage 2019
9 Cross-Correlation + gradient 0.492 19.82 0.59 0.79 ✓ Certified Thévenaz et al., IEEE TIP 1998

Visible Data Fields

y H_ideal spec_ranges

Dataset

Format: HDF5
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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