Due to the complexity of the anatomical structure for human organs, medical image segmentation is always a challenging computer vision task. The Convolutional Neural Network (CNN) requires a rich feature representation, which not only needs the convolutional layers from shallow to deep,but also requires the resolution from small to large. Although CNN can be used to fuse mid-level features that are employed short-cutting, this just is a simple “shallow” connection. Thus, how to obtain useful features and how to utilize these features to improve the segmentation processes are still the key issues. In this paper, Multi-features Refinement and Aggregation (MRA) makes full use of hierarchical features by using the features fusion on several levels, and reveal the importance of refinement and aggregation of features in the medical image segmentation process. The network get low-level, high-level and even mid-level features by sampling. After aggregation and re-extraction, these features are more effectively combined. Experiment results show that our method can significantly improve segmentation accuracy compared to existing feature fusion schemes. And our approach is generalized to different backbone networks with consistent accuracy gain in brain segmentation, and it sets a new state-of-the-art in the Brat-2015 benchmarks.

中文翻译：

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医学大脑分割的多特征细化和聚合

由于人体器官解剖结构的复杂性，医学图像分割一直是一项具有挑战性的计算机视觉任务。卷积神经网络（CNN）需要丰富的特征表示，不仅需要卷积层由浅到深，还需要分辨率从小到大。尽管 CNN 可用于融合采用捷径的中级特征，但这只是一个简单的“浅”连接。因此，如何获得有用的特征以及如何利用这些特征来改进分割过程仍然是关键问题。在本文中，多特征细化和聚合（MRA）通过在多个层次上进行特征融合，充分利用了分层特征，并揭示了在医学图像分割过程中细化和聚合特征的重要性。网络通过采样得到低级、高级甚至中级特征。经过聚合和重新提取，这些特征被更有效地结合起来。实验结果表明，与现有的特征融合方案相比，我们的方法可以显着提高分割精度。我们的方法被推广到不同的骨干网络，并在大脑分割中获得一致的精度增益，并在 Brat-2015 基准测试中设置了新的最新技术。实验结果表明，与现有的特征融合方案相比，我们的方法可以显着提高分割精度。我们的方法被推广到不同的骨干网络，并在大脑分割中获得一致的精度增益，并在 Brat-2015 基准测试中设置了新的最新技术。实验结果表明，与现有的特征融合方案相比，我们的方法可以显着提高分割精度。我们的方法被推广到不同的骨干网络，在大脑分割中获得一致的精度增益，并在 Brat-2015 基准测试中设置了新的最新技术。