DOI: 10.5281/zenodo.14478209
title: OCT Dataset for Segmentation of Atherosclerotic Plaque Morphological Features

description: Objectives: The primary goal of this dataset is to enable the automated segmentation and quantification of atherosclerotic plaque features in OCT images. Cardiovascular disease, with atherosclerosis at its core, remains a global health challenge. Accurate identification of vulnerable plaques is crucial for preventing acute cardiovascular events such as myocardial infarction and stroke. OCT imaging provides high-resolution insights into plaque morphology but is often constrained by manual interpretation challenges. This dataset, curated with diverse annotations of key plaque morphological features, aims to facilitate the development and evaluation of machine learning models for precise plaque analysis. By advancing segmentation capabilities, this dataset contributes to improved diagnostics and therapeutic strategies in cardiovascular care.

Ethical Approval: The dataset complies with ethical standards, adhering to the Declaration of Helsinki. Ethical approval was granted by the Local Ethical Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russia) under protocol code 2022/06 (approved on June 30, 2022). All participants provided informed consent. Data collection involved patients aged 18 years or older, ensuring balanced gender representation and inclusion of various comorbid conditions for comprehensive clinical relevance (refer to Table 1).

Description: The dataset consists of OCT images acquired from 103 patients across two cardiovascular research centers. These images, collected over one year, represent a diverse array of imaging devices and patient demographics. The dataset includes 25,698 annotated slices, each capturing key plaque morphological features. These features include lumen (LM), fibrous cap (FC), lipid core (LC), and vasa vasorum (VV). The images vary in dimensions from 704 x 704 to 1024 x 1024 pixels, reflecting differences in anatomical characteristics and imaging conditions. Annotations were performed using Supervisely, with meticulous double-verification processes to ensure accuracy.

Annotation Method: Two cardiologists annotated the dataset, identifying plaque features using binary masks. The annotations underwent a review and double-verification by a senior cardiologist and technical specialist, enhancing precision and consistency. The morphological features segmented include the vascular lumen, fibrous cap, lipid core, and vasa vasorum, each providing critical insights into plaque stability and cardiovascular risk.

Dataset Split: A 5-fold cross-validation technique was employed for dataset splitting, ensuring robust model evaluation while preventing data leakage. Approximately 80% of images were allocated for training in each fold, with the remaining 20% reserved for testing (refer to Table 2). This method allowed a balanced and comprehensive assessment of segmentation performance across the dataset.

Access to the Study: Further information about this study, including curated source code, dataset details, and trained models, can be accessed through the following repositories:



Source Code: https://github.com/ViacheslavDanilov/oct_segmentation

Dataset: https://doi.org/10.5281/zenodo.14478209

Models: https://doi.org/10.5281/zenodo.14481678


 

Table 1. Baseline characteristics of patients included in the study.








Parameter



Value





Sex:



 





Male, n (%)



77 (74.7)





Female, n (%)



26 (25.3)





Median Age, years [min – max]



69 [43 – 83]





Arterial hypertension, n (%)



92 (89.3)





Diabetes Mellitus, n (%)



22 (21.4)





Myocardial Infarction, n (%)



22 (21.4)





Polyvascular Disease, n (%)



29 (28.2)





Angina Pectoris:



 





Silent ischemia, n (%)



9 (8.7)





Functional class 1, n (%)



24 (23.3)





Functional class 2, n (%)



55 (53.4)





Functional class 3, n (%)



15 (14.6)






 

Table 2. Image and plaque morphological feature distributions across folds and subsets.






Fold
Subset
LM
FC
LC
VV
Total objects
Total images


1
Train
17264
5610
5576
328
28778
16901


1
Test
4544
1616
1616
122
7898
4492


2
Train
17554
5709
5690
237
29190
17207


2
Test
4254
1517
1502
213
7486
4186


3
Train
17220
5600
5565
407
28792
16962


3
Test
4588
1626
1627
43
7884
4431


4
Train
17813
5724
5686
416
29639
17473


4
Test
3995
1502
1506
34
7037
3920


5
Train
17381
6261
6251
412
30405
17029


5
Test
4427
965
941
38
6371
4364