  One of the time-consuming routine work for a radiologist is to discern
anatomical structures from tomographic images. For assisting radiologists, this
paper develops an automatic segmentation method for pelvic magnetic resonance
(MR) images. The task has three major challenges 1) A pelvic organ can have
various sizes and shapes depending on the axial image, which requires local
contexts to segment correctly. 2) Different organs often have quite similar
appearance in MR images, which requires global context to segment. 3) The
number of available annotated images are very small to use the latest
segmentation algorithms. To address the challenges, we propose a novel
convolutional neural network called Attention-Pyramid network (APNet) that
effectively exploits both local and global contexts, in addition to a
data-augmentation technique that is particularly effective for MR images. In
order to evaluate our method, we construct fine-grained (50 pelvic organs) MR
image segmentation dataset, and experimentally confirm the superior performance
of our techniques over the state-of-the-art image segmentation methods.
