  Boundary incompleteness raises great challenges to automatic prostate
segmentation in ultrasound images. Shape prior can provide strong guidance in
estimating the missing boundary, but traditional shape models often suffer from
hand-crafted descriptors and local information loss in the fitting procedure.
In this paper, we attempt to address those issues with a novel framework. The
proposed framework can seamlessly integrate feature extraction and shape prior
exploring, and estimate the complete boundary with a sequential manner. Our
framework is composed of three key modules. Firstly, we serialize the static 2D
prostate ultrasound images into dynamic sequences and then predict prostate
shapes by sequentially exploring shape priors. Intuitively, we propose to learn
the shape prior with the biologically plausible Recurrent Neural Networks
(RNNs). This module is corroborated to be effective in dealing with the
boundary incompleteness. Secondly, to alleviate the bias caused by different
serialization manners, we propose a multi-view fusion strategy to merge shape
predictions obtained from different perspectives. Thirdly, we further implant
the RNN core into a multiscale Auto-Context scheme to successively refine the
details of the shape prediction map. With extensive validation on challenging
prostate ultrasound images, our framework bridges severe boundary
incompleteness and achieves the best performance in prostate boundary
delineation when compared with several advanced methods. Additionally, our
approach is general and can be extended to other medical image segmentation
tasks, where boundary incompleteness is one of the main challenges.
