Auto3DSeg for Brain Tumor Segmentation from 3D MRI in BraTS 2023 Challenge

2025/10/29 by Myronenko, Andriy, Yang, Dong, He, Yufan +1
#Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2510.25058

Abstract

In this work, we describe our solution to the BraTS 2023 cluster of challenges using Auto3DSeg from MONAI. We participated in all 5 segmentation challenges, and achieved the 1st place results in three of them: Brain Metastasis, Brain Meningioma, BraTS-Africa challenges, and the 2nd place results in the remaining two: Adult and Pediatic Glioma challenges.

Citations

• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
• Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images
• Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis
• DiNTS: Differentiable Neural Network Topology Search for 3D Medical Image Segmentation
• Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
• 3D MRI brain tumor segmentation using autoencoder regularization

Cited by

• Knowledge-Guided Brain Tumor Segmentation via Synchronized Visual-Semantic-Topological Prior Fusion

Related

• BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
• BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023
• 3D AGSE-VNet: An Automatic Brain Tumor MRI Data Segmentation Framework
• The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
• Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs