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

arXiv: https://arxiv.org/abs/2503.09885 · HTML: https://mlsanity.com/paper/2503.09885/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 17 of 17)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning](https://mlsanity.com/paper/2003.04696/) (2020)
- [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://mlsanity.com/paper/2301.11798/) (2023)
- [Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2111.12940/) (2021)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [Using DUCK-Net for Polyp Image Segmentation](https://mlsanity.com/paper/2311.02239/) (2023)
- [Active learning for medical image segmentation with stochastic batches](https://mlsanity.com/paper/2301.07670/) (2023)
- [MedAL: Deep Active Learning Sampling Method for Medical Image Analysis](https://mlsanity.com/paper/1809.09287/) (2018)
- [pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis](https://mlsanity.com/paper/2010.03639/) (2020)
- [Adaptive Superpixel for Active Learning in Semantic Segmentation](https://mlsanity.com/paper/2303.16817/) (2023)
- [COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12004/) (2023)
- [Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.16298/) (2024)
- [ReBound: An Open-Source 3D Bounding Box Annotation Tool for Active Learning](https://mlsanity.com/paper/2303.06250/) (2023)
- [Predictive Accuracy-Based Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2405.00452/) (2024)

## 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