  Segmentation is a prerequisite yet challenging task for medical image
analysis. In this paper, we introduce a novel deeply supervised active learning
approach for finger bones segmentation. The proposed architecture is fine-tuned
in an iterative and incremental learning manner. In each step, the deep
supervision mechanism guides the learning process of hidden layers and selects
samples to be labeled. Extensive experiments demonstrated that our method
achieves competitive segmentation results using less labeled samples as
compared with full annotation.
