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

arXiv 2503.09885 ↗First posted 12 Mar 2025eess.IV cs.CV cs.HCcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 12 Mar 2025.

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References · 17

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  3. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  4. 2018nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentationcited by 291
  5. 2020TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learningcited by 176
  6. 2023MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformercited by 124
  7. 2021Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentationcited by 56
  8. 2021Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentationcited by 44
  9. 2023Using DUCK-Net for Polyp Image Segmentationcited by 30
  10. 2023Active learning for medical image segmentation with stochastic batchescited by 20
  11. 2018MedAL: Deep Active Learning Sampling Method for Medical Image Analysiscited by 19
  12. 2020pymia: A Python package for data handling and evaluation in deep learning-based medical image analysiscited by 16
5 more
  1. 2023Adaptive Superpixel for Active Learning in Semantic Segmentationcited by 14
  2. 2023COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentationcited by 9
  3. 2024Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentationcited by 9
  4. 2023ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learningcited by 4
  5. 2024Predictive Accuracy-Based Active Learning for Medical Image Segmentationcited by 3

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