Simplifying 3D Medical Imaging with MONAI Auto3DSeg


In the rapidly evolving field of medical imaging research, developers and researchers play a crucial role in advancing the state of the art. With over 60% of the latest MICCAI conference papers centered on segmentation algorithms for 3D datasets, there’s a growing demand for tools that empower developers to tackle the complex task of 3D medical image segmentation.

Auto3DSeg is the answer to this demand. Designed with developers in mind, it seamlessly bridges the gap between innovation and practical application, providing an efficient and user-friendly solution. By harnessing the capabilities of MONAI and modern GPU technology, Auto3DSeg empowers developers — both novices and experts — to achieve hassle-free, state-of-the-art performance in 3D medical image segmentation.

Understanding MONAI Auto3DSeg

Auto3DSeg is a MONAI native project, aiming to demonstrate best practices of common 3D segmentation workflows for several algorithms. For non-expert users, it allows them to start with only a few lines of code to automatically train models on their 3D CT or MRI data. For expert users, it provides recipes of best practices for segmentation training with MONAI components, to achieve state-of-the-art baseline segmentation performance, customize it, and further build upon it. Special efforts were put into improving the computational performance of Auto3DSeg, focusing on minimizing training and inference time while maximizing GPU compute utilization.

Key Features:

  • Dataset Analysis: Auto3DSeg sets the stage for subsequent steps by analyzing the dataset’s intensity, size, and spacing.
  • Algorithm Generation: Algorithm folders are automatically configured based on initial data assessment.
  • GPU Integration: Innate GPU support accelerates model training, validation, and inference.
  • Hyper-parameter Optimization: Auto3DSeg refines model parameters for optimal performance and accuracy.
  • Model Ensemble: Auto3DSeg creates and integrates multiple models, enhancing accuracy and reliability.

Real-world Applications

Let’s explore Auto3DSeg’s capabilities through recent challenges. A team of NVIDIA researchers successfully applied Auto3DSeg in several recent MICCAI 2023 challenges.

The team members are Andriy Myronenko, Dong Yang, Yufan He, and Daguang Xu.

BraTS 2023–Multiple 1st and 2nd Place wins: Auto3DSeg showcased its abilities placing 1st and 2nd in multiple BraTS competitions. (1st Place in Brain Metastases, 1st place in Brain Meningioma, 1st place in Brats-Africa Glioma, 2nd place in Adult Glioma, 2nd place in Pediatric Glioma). See the leaderboardhere:!Synapse:syn51156910/wiki/621282

KiTS 2023–1st Place: Auto3DSeg excelled in the KiTS 23 segmentation challenge at MICCAI 2023, achieving top-tier performance in 3D kidney segmentation. See the leaderboard here:

SEG.A. 2023–1st Place: Auto3DSeg demonstrated adaptability and robustness by winning the Aorta segmentation challenge at MICCAI 2023. See the leaderboard here:

MVSEG 2023–1st Place: In the MVSEG23 challenge, Auto3DSeg showcased its versatility by securing the top spot in segmenting mitral valve leaflets from 3D echocardiography volumes. See the leaderboard here:!Synapse:syn51186045/wiki/622048.

Here are a few images from the segmentation challenges above:

BraTS 2023 Images
KiTS 2023 Images
SEG.A. 2023 Images
MVSEG 2023 Images

Previous Challenge Achievements

Auto3DSeg’s track record includes:

  • 1st Place in MICCAI 2022 challenge HECKTOR 2022 for head and neck tumor segmentation in PET/CT images.
  • 2nd Place in MICCAI 2022 challenge INSTANCE22 for intracranial hemorrhage segmentation on Non-Contrast head CT (NCCT), ranking first in Dice score.
  • 2nd Place in MICCAI 2022 challenge ISLES’22 for ischemic stroke lesion segmentation, ranking first in Dice score.

Learn More

For a comprehensive understanding of Auto3DSeg, check out our resources:

YouTube Walkthrough: Dive deeper into Auto3DSeg’s mechanics and advantages.

GitHub Tutorials: Explore detailed tutorials to unlock the full potential of this transformative tool.

By streamlining 3D medical image segmentation with MONAI Auto3DSeg, developers and researchers can make significant strides in medical diagnosis, treatment planning, and research.



MONAI Medical Open Network for AI

MONAI framework is a PyTorch-based open-source foundation for deep learning in healthcare imaging. It is domain-optimized, freely available and community backed