A Multi-resolution Coarse-to-Fine Segmentation Framework with Active Learning in 3D Brain MRI

摘要

Precise segmentation of key tissues in medical images is of great significance. Although deep neural networks have achieved promising results in many medical image segmentation tasks, it is still a challenge for volumetric medical image segmentation due to the limited computing resources and annotated datasets. In this paper, we propose a multi-resolution coarse-to-fine segmentation framework to perform accurate segmentation. The proposed framework contains a coarse stage and a fine stage. The coarse stage with low-resolution data provide high semantic cues for the fine stage. Moreover, we embed active learning processes into coarse-to-fine framework for sparse annotation, the proposed multiple query criteria active learning methods can select high-value slices to label. We evaluated the effectiveness of proposed framework on two public brain MRI datasets. Our coarse-to-fine networks outperform other competitive methods under the condition of fully supervised training. In addition, the proposed active learning method only need 30% to 40% slices of one scan to produce relatively better dense prediction results than non-active learning method and one query criteria active learning methods.