开源工具让医生和研究者一键使用顶尖脑肿瘤图像分析模型。
BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
- 提供一键调用的Python包，集成多个竞赛优胜算法。
- 支持多种脑肿瘤类型，无需复杂编程即可完成分割与合成。
- 适合临床医生、放射科医师及无编程背景的研究人员使用。
脑肿瘤分割（BraTS）挑战赛通过提供大规模、精心标注的数据集，推动了脑肿瘤图像分析的发展。然而，尽管其成果广泛认可，相关算法在科研与临床中的实际应用仍有限。为加速技术传播，本文提出BraTS orchestrator——一个开源Python工具包，可无缝访问来自BraTS挑战赛生态系统的先进分割与生成算法。该工具包部署于GitHub（https://github.com/BrainLesion/BraTS），配备直观教程，面向编程基础薄弱的用户，使研究人员与临床医生能轻松部署竞赛优胜模型进行推理。通过抽象现代深度学习的复杂性，BraTS orchestrator降低了技术门槛，使神经影像学与神经肿瘤学领域的更广泛群体都能便捷获取这些前沿成果。
原文摘要 · Abstract (English)
The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically relevant tasks. However, despite its success and popularity, algorithms and models developed through BraTS have seen limited adoption in both scientific and clinical communities. To accelerate their dissemination, we introduce BraTS orchestrator, an open-source Python package that provides seamless access to state-of-the-art segmentation and synthesis algorithms for diverse brain tumors from the BraTS challenge ecosystem. Available on GitHub (https://github.com/BrainLesion/BraTS), the package features intuitive tutorials designed for users with minimal programming experience, enabling both researchers and clinicians to easily deploy winning BraTS algorithms for inference. By abstracting the complexities of modern deep learning, BraTS orchestrator democratizes access to the specialized knowledge developed within the BraTS community, making these advances readily available to broader neuro-radiology and neuro-oncology audiences.
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