Probabilistic atlas priors have been commonly used to derive adaptive and
robust brain MRI segmentation algorithms. Widely-used neuroimage analysis
pipelines rely heavily on these techniques, which are often computationally
expensive. In contrast, there has been a recent surge of approaches that
leverage deep learning to implement segmentation tools that are computationally
efficient at test time. However, most of these strategies rely on learning from
manually annotated images. These supervised deep learning methods are therefore
sensitive to the intensity profiles in the training dataset. To develop a deep
learning-based segmentation model for a new image dataset (e.g., of different
contrast), one usually needs to create a new labeled training dataset, which
can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or
augmentation approaches. In this paper, we propose an alternative strategy that
combines a conventional probabilistic atlas-based segmentation with deep
learning, enabling one to train a segmentation model for new MRI scans without
the need for any manually segmented images. Our experiments include thousands
of brain MRI scans and demonstrate that the proposed method achieves good
accuracy for a brain MRI segmentation task for different MRI contrasts,
requiring only approximately 15 seconds at test time on a GPU. The code is
freely available at this http URL

本文提出了一种结合传统概率性图谱分割和深度学习的策略，可以不需要手动标注映像即可为新的 MRI 扫描训练分割模型，在多种 MRI 对比度下，已经在成千上万个 MRI 扫描中通过实验证明了良好的准确性，并在 GPU 上测试只需要约 15 秒。