Cross-Task Data Augmentation by Pseudo-label Generation for Region Based Coronary Artery Instance Segmentation

TL;DR

This paper proposes a novel cross-task data augmentation method using pseudo-labels from related tasks to improve coronary artery segmentation, significantly enhancing model performance despite limited data.

Contribution

It introduces a pseudo-label generation approach from related tasks to augment data and improve segmentation accuracy in coronary artery imaging.

Findings

F1 score increased by 9% on validation data

F1 score increased by 3% on test data

Demonstrates effectiveness of cross-task pseudo-label augmentation

Abstract

Coronary Artery Diseases (CADs) although preventable, are one of the leading causes of death and disability. Diagnosis of these diseases is often difficult and resource intensive. Angiographic imaging segmentation of the arteries has evolved as a tool of assistance that helps clinicians make an accurate diagnosis. However, due to the limited amount of data and the difficulty in curating a dataset, the task of segmentation has proven challenging. In this study, we introduce the use of pseudo-labels to address the issue of limited data in the angiographic dataset to enhance the performance of the baseline YOLO model. Unlike existing data augmentation techniques that improve the model constrained to a fixed dataset, we introduce the use of pseudo-labels generated on a dataset of separate related task to diversify and improve model performance. This method increases the baseline F1 score by 9% in the validation data set and by 3% in the test data set.