  Medical image segmentation is a fundamental task in medical image analysis.
Despite that deep convolutional neural networks have gained stellar performance
in this challenging task, they typically rely on large labeled datasets, which
have limited their extension to customized applications. By revisiting the
superiority of atlas based segmentation methods, we present a new framework of
One-pass aligned Atlas Set for Images Segmentation (OASIS). To address the
problem of time-consuming iterative image registration used for atlas warping,
the proposed method takes advantage of the power of deep learning to achieve
one-pass image registration. In addition, by applying label constraint, OASIS
also makes the registration process to be focused on the regions to be
segmented for improving the performance of segmentation. Furthermore, instead
of using image based similarity for label fusion, which can be distracted by
the large background areas, we propose a novel strategy to compute the label
similarity based weights for label fusion. Our experimental results on the
challenging task of prostate MR image segmentation demonstrate that OASIS is
able to significantly increase the segmentation performance compared to other
state-of-the-art methods.
