DOI: 10.5281/zenodo.18670884
title: Synthetic Benchmark Dataset for OCT Similarity Metrics: Controlled Micro-, Meso-, and Macro-Structural Shifts

description: Overview

This dataset contains synthetic Optical Coherence Tomography (OCT) B-scans and associated physical parameter maps generated via a rigorous physics-based simulation. It serves as a benchmark for evaluating the performance of similarity metrics (e.g., MSE, SSIM, MS-SSIM, LPIPS, PSNR, VIF) in the context of optical coherence tomography.

The dataset was generated to support the MICCAI 2026 submission: "Benchmarking OCT Scans Similarity Metrics via Physics-Based Simulatory Analysis with Controlled Micro-, Meso-, and Macro-Structural Shifts."

Dataset Structure

The data is organized into three hierarchical categories representing different biological scales:

1. Micro-structure (Scatterer Statistics)

Tests sensitivity to sub-resolution scatterer density changes under constant backscattering amplitude.

Dens_200000: Reference standard tissue (200,000 scatterers).

Dens_50000: Low-density tissue (50,000 scatterers).

2. Meso-structure (Structural Inclusions)

Tests sensitivity to discrete inclusions (e.g., vessels, cysts etc) embedded in the reference tissue.

Meso_Dens: Inclusions defined by reduced scatterer density (0.1x of background).

Meso_Amp: Inclusions defined by reduced backscattering amplitude (0.1x of background).

Meso_Both: Inclusions defined by both reduced density (0.2x) and amplitude (0.2x).

3. Macro-structure (Layer Morphology)

Tests sensitivity to morphological layer changes (e.g., epithelial thickness) on a complex heterogeneous background.

Macro_Thin: Two-layer tissue with a thin top layer (Boundary at 240 µm).

Macro_Thick: Two-layer tissue with a thick top layer (Boundary at 480 µm).

Both macro sets feature a dim top layer (0.1x amplitude) and underlying meso-inclusions in the bottom layer to simulate complex clinical scenarios.

File Contents

Each scan folder contains:

Scan_X.txt: Ground truth coordinates and amplitudes of discrete scatterers.

Scan_X.png: The synthetic OCT structural B-scan.

Scan_X_OAC.png: Optical Attenuation Coefficient map.

Scan_X_SC.png: Speckle Contrast map.

Scan_X_RSC.png: Refined Speckle Contrast map.

 

Additional dataset for sensitivity evaluation - Metrics_Sensitivity_Benchmark_Grading_structural_changes.zip:This dataset contains physics-based synthetic OCT B-scans designed to evaluate the sensitivity of metrics to structural changes against a background of speckle noise. The archive is organized into three main sections. First, a 'Baseline' folder containing 20 independent speckle realizations of a fixed structure (400 micron layer thickness, 20 void inclusions) to establish the noise floor. Second, 'Series1_Macro_Thickness', which contains 10 steps of graded macro-structural changes where the epithelial layer thickness decreases from 380 microns to 200 microns (approaching a 2x reduction). Third, 'Series2_Meso_Count', which contains graded steps of meso-structural changes where the number of inclusions increases from 20 to 39 (approaching ~2x increase). Each step in these series contains 20 independent scans to allow for statistical analysis of metric distributions.

 

This dataset is made freely available for any reuse. Use of this dataset requires appropriate citation of both this dataset (https://doi.org/10.5281/zenodo.18670884) and the associated papers:

 

Methodology References

[1] Sovetsky, A., Matveyev, A., Chizhov, P., Zaitsev, V., Matveev, L. (2025). OCT Scans Simulation Framework for Data Augmentation and Controlled Evaluation of Signal Processing Approaches. In: Fernandez, V., Wolterink, J.M., Wiesner, D., Remedios, S., Zuo, L., Casamitjana, A. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2024. Lecture Notes in Computer Science, vol 15187. Springer, Cham.https://doi.org/10.1007/978-3-031-73281-2_12

[2] Nikoshin, D., Mikhailenko, D., Sovetsky, A., Matveyev, A., Zaitsev, V., Matveev, L. (2026). From Tissue-Mimicking Phantoms to Physics-Based Scans: Synthetic OCT for Few-Shot Foundation Model Training. In: Fernandez, V., Wiesner, D., Zuo, L., Casamitjana, A., Remedios, S.W. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2025. Lecture Notes in Computer Science, vol 16085. Springer, Cham.https://doi.org/10.1007/978-3-032-05573-6_5

[3] MICCAI 2026 Submission (will be availiable soon)

 

Repo: https://github.com/SynthOCTChallenge/SynthOCT_Baseline

 

FundingThis dataset was curated and published with the support of the Russian Science Foundation (RSF):RSF Grant № 25-12-20032: "New Approaches to the Development of Algorithms for Analyzing OCT Scans: Modification and Optimization of Large Models Based on Physical Principles and Conditions of OCT Signal Formation" (https://rscf.ru/en/project/25-12-20032/)