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One of the time-consuming routine work for a radiologist is to discernanatomical structures from tomographic images. For assisting radiologists, thispaper develops an automatic segmentation method for pelvic magnetic resonance(MR) images. The task has three major challenges 1) A pelvic organ can havevarious sizes and shapes depending on the axial image, which requires localcontexts to segment correctly. 2) Different organs often have quite similarappearance in MR images, which requires global context to segment. 3) Thenumber of available annotated images are very small to use the latestsegmentation algorithms. To address the challenges, we propose a novelconvolutional neural network called Attention-Pyramid network (APNet) thateffectively exploits both local and global contexts, in addition to adata-augmentation technique that is particularly effective for MR images. Inorder to evaluate our method, we construct fine-grained (50 pelvic organs) MRimage segmentation dataset, and experimentally confirm the superior performanceof our techniques over the state-of-the-art image segmentation methods.

Combining Pyramid Pooling and Attention Mechanism for Pelvic MR Image Semantic Segmentaion