  Despite recent progress of automatic medical image segmentation techniques,
fully automatic results usually fail to meet the clinical use and typically
require further refinement. In this work, we propose a quality-aware memory
network for interactive segmentation of 3D medical images. Provided by user
guidance on an arbitrary slice, an interaction network is firstly employed to
obtain an initial 2D segmentation. The quality-aware memory network
subsequently propagates the initial segmentation estimation bidirectionally
over the entire volume. Subsequent refinement based on additional user guidance
on other slices can be incorporated in the same manner. To further facilitate
interactive segmentation, a quality assessment module is introduced to suggest
the next slice to segment based on the current segmentation quality of each
slice. The proposed network has two appealing characteristics: 1) The
memory-augmented network offers the ability to quickly encode past segmentation
information, which will be retrieved for the segmentation of other slices; 2)
The quality assessment module enables the model to directly estimate the
qualities of segmentation predictions, which allows an active learning paradigm
where users preferentially label the lowest-quality slice for multi-round
refinement. The proposed network leads to a robust interactive segmentation
engine, which can generalize well to various types of user annotations (e.g.,
scribbles, boxes). Experimental results on various medical datasets demonstrate
the superiority of our approach in comparison with existing techniques.
