QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image Segmentation

2025-03-12

AI Summary

Medical AI models for imaging often face deployment barriers due to incompatibility with clinical viewing systems (e.g., DICOM viewers) and suffer from time-consuming, low-reproducibility manual annotation. To address these challenges, we propose QuickDraw—an open-source, clinically integrated AI framework. Built on PyQt, ITK, and VTK, it enables cross-platform visualization and seamless integration with state-of-the-art segmentation models (e.g., MONAI), supporting DICOM ingestion, real-time 3D segmentation, interactive editing, and quantitative evaluation. Its core innovation is a human–model collaborative annotation paradigm coupled with uncertainty-sampling–driven active learning, establishing a closed-loop workflow for iterative model refinement. Experiments demonstrate that per-case CT segmentation time decreases from 4 hours to 6 minutes, achieving a 10% speedup over existing AI-assisted approaches. User studies confirm high usability and clinical utility.

Technology Category

• Computer Vision: Segmentation
• Humans and AI: AI for Accessibility
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Analyzing CT scans, MRIs and X-rays is pivotal in diagnosing and treating diseases. However, detecting and identifying abnormalities from such medical images is a time-intensive process that requires expert analysis and is prone to interobserver variability. To mitigate such issues, machine learning-based models have been introduced to automate and significantly reduce the cost of image segmentation. Despite significant advances in medical image analysis in recent years, many of the latest models are never applied in clinical settings because state-of-the-art models do not easily interface with existing medical image viewers. To address these limitations, we propose QuickDraw, an open-source framework for medical image visualization and analysis that allows users to upload DICOM images and run off-the-shelf models to generate 3D segmentation masks. In addition, our tool allows users to edit, export, and evaluate segmentation masks to iteratively improve state-of-the-art models through active learning. In this paper, we detail the design of our tool and present survey results that highlight the usability of our software. Notably, we find that QuickDraw reduces the time to manually segment a CT scan from four hours to six minutes and reduces machine learning-assisted segmentation time by 10% compared to prior work. Our code and documentation are available at https://github.com/qd-seg/quickdraw

Problem

• Automates medical image segmentation to reduce time and cost
• Integrates machine learning models with existing medical image viewers
• Enables active learning to iteratively improve segmentation accuracy

Innovation

• Open-source framework for medical image visualization
• Integration of off-the-shelf models for 3D segmentation
• Active learning for iterative model improvement