Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation

TL;DR

This paper presents a combined AI and crowdsourcing framework that enhances medical image datasets by improving annotation quality and quantity, thereby boosting deep learning model performance in medical image segmentation.

Contribution

It introduces a versatile platform integrating AI, crowdsourcing, and generative models to efficiently create high-quality, expanded medical image datasets for segmentation tasks.

Findings

Improved segmentation accuracy with the enhanced dataset.

Faster annotation process using AI-assisted crowdsourcing.

Effective dataset expansion with synthetic images.

Abstract

Recent advancements in medical imaging and artificial intelligence (AI) have greatly enhanced diagnostic capabilities, but the development of effective deep learning (DL) models is still constrained by the lack of high-quality annotated datasets. The traditional manual annotation process by medical experts is time- and resource-intensive, limiting the scalability of these datasets. In this work, we introduce a robust and versatile framework that combines AI and crowdsourcing to improve both the quality and quantity of medical image datasets across different modalities. Our approach utilises a user-friendly online platform that enables a diverse group of crowd annotators to label medical images efficiently. By integrating the MedSAM segmentation AI with this platform, we accelerate the annotation process while maintaining expert-level quality through an algorithm that merges crowd-labelled images. Additionally, we employ pix2pixGAN, a generative AI model, to expand the training dataset with synthetic images that capture realistic morphological features. These methods are combined into a cohesive framework designed to produce an enhanced dataset, which can serve as a universal pre-processing pipeline to boost the training of any medical deep learning segmentation model. Our results demonstrate that this framework significantly improves model performance, especially when training data is limited.