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The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
Hongwei Bran Li1,2,3, Gian Marco Conte4, Qingqiao Hu1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45
1University of Zurich, Switzerland.
Arxiv
|August 23, 2023
Summary
This study introduces the Brain MR Image Synthesis Benchmark (BraSyn) to generate missing MRI sequences for improved brain tumor segmentation. The benchmark aims to enhance automated segmentation pipelines by enabling realistic image synthesis from available modalities.
Area of Science:
- Medical image analysis
- Artificial intelligence in healthcare
- Neuroimaging
Background:
- Automated brain tumor segmentation relies on multiple MRI modalities (T1, T1-contrast, T2, FLAIR).
- Missing MRI sequences in clinical practice hinder the performance of segmentation algorithms.
- Developing methods to substitute missing modalities is crucial for wider clinical adoption.
Purpose of the Study:
- Establish the Brain MR Image Synthesis Benchmark (BraSyn) for evaluating MRI modality synthesis.
- Facilitate the development of robust automated brain tumor segmentation pipelines.
- Promote realistic generation of missing MRI sequences using available imaging data.
Main Methods:
- The Brain MR Image Synthesis Benchmark (BraSyn) was established for the MICCAI 2023 challenge.
- The benchmark focuses on evaluating image synthesis methods for generating missing MRI modalities.
- A diverse, multi-modal dataset from various institutions was utilized.
Main Results:
- The benchmark provides a platform to assess the performance of image synthesis techniques.
- It enables the realistic generation of missing MRI sequences.
- The ultimate goal is to improve automated brain tumor segmentation.
Conclusions:
- The BraSyn benchmark is essential for advancing automated brain tumor segmentation.
- Realistic MRI synthesis can overcome data limitations in clinical settings.
- This work supports the broader integration of AI in neuro-oncology workflows.
