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

2024-09-04

arXiv.org

AI Summary

Medical image segmentation is hindered by the scarcity of high-quality annotated data and the prohibitively high cost of expert annotation. To address this, we propose a crowdsourcing-enhanced framework that synergistically integrates artificial intelligence (AI) and citizen science. Our approach features a cross-modal preprocessing–enabled crowdsourcing annotation platform; AI-assisted initial screening via MedSAM; high-fidelity synthetic data generation using pix2pixGAN; and a novel multi-source label fusion and quality assurance mechanism—establishing an end-to-end “annotate–optimize–synthesize–validate” pipeline. This framework effectively alleviates the small-sample bottleneck: on multimodal medical imaging datasets, it achieves an average 12.3% improvement in Dice coefficient for segmentation and a fivefold increase in annotation efficiency. The proposed paradigm offers a scalable, reproducible solution for training robust segmentation models in low-resource settings.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: Crowd Sourcing and Human Computation
• Machine Learning: Multimodal Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Lack of high-quality annotated medical image datasets
• Time- and resource-intensive manual annotation process
• Limited scalability of traditional annotation methods

Innovation

• Combines AI and crowdsourcing for medical image annotation
• Uses MedSAM and pix2pixGAN to enhance dataset quality
• Integrates user-friendly platform for efficient crowd labeling