用纯合成数据训练脑部分割模型，无需真实图像或解剖先验
Skull stripping with purely synthetic data
- 基于纯合成数据训练，不依赖真实脑影像或标注
- 在多模态、多物种及病理情况下达到可比准确率
- 为通用医学图像分割提供新方向，适合无真实数据场景
尽管已有多种针对多模态和多物种的去颅骨算法，但仍缺乏根本上可泛化的方案。本文提出PUMBA（PUrely synthetic Multimodal/species invariant Brain extrAction），一种仅通过合成数据训练脑提取模型的方法，无需真实脑图像或标签。结果表明，即使不使用任何真实图像或解剖先验，该模型在多模态、多物种及病理情况下仍能达到可比较的准确性。这项工作为任意通用医学图像分割任务开辟了新研究方向。
原文摘要 · Abstract (English)
While many skull stripping algorithms have been developed for multi-modal and multi-species cases, there is still a lack of a fundamentally generalizable approach. We present PUMBA(PUrely synthetic Multimodal/species invariant Brain extrAction), a strategy to train a model for brain extraction with no real brain images or labels. Our results show that even without any real images or anatomical priors, the model achieves comparable accuracy in multi-modal, multi-species and pathological cases. This work presents a new direction of research for any generalizable medical image segmentation task.
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