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.

翻译：放射学家的一项耗时的日常工作是从成像图像中辨别解剖结构。为了协助放射学家,本文件为骨盆磁共振图像开发了自动分离方法。任务有三大挑战:(1) 骨盆器官的大小和形状可以视轴图象的不同而不同,这要求地方环境进行正确分解。(2) 不同器官在光学光学光学图像中往往具有非常相似的外观,这要求从全球角度对段进行分解。(3) 现有附加说明的图像数量非常少,无法使用最新的分解算法。为了应对挑战,我们提议建立一个名为“注意-金字塔网络”的新型神经网络,除了对光学光学光学图像特别有效的数据增强技术外,还有效地利用当地和全球环境。为了评估我们的方法,我们制作了精细的(50个骨盆器官)光谱图像分解数据集,并实验性地证实我们技术在状态图像分解方法上的优异性。