Automatic segmentation of prostate magnetic resonance (MR) images has great significance for the diagnosis and clinical application of prostate diseases. It faces enormous challenges because of the low contrast of the tissue boundary and the small effective area of the prostate MR images. In order to solve these problems, we propose a novel end-to-end professional network which consists of an Encoder-Decoder structure with dense dilated spatial pyramid pooling (DDSPP) for prostate segmentation based on deep learning. First, the DDSPP module is used to extract the multi-scale convolution features in the prostate MR images, and then the decoder is used to capture the clear boundary of prostate. Competitive results are produced over state of the art on 130 MR images which key metrics Dice similarity coefficient (DSC) and Hausdorff distance (HD) are 0.954 and 1.752 mm respectively. Experimental results show that our method has high accuracy and robustness.

前列腺磁共振（MR）图像的自动分割对前列腺疾病的诊断和临床应用具有重要意义。由于组织边界的对比度低以及前列腺MR图像的有效面积小，它面临着巨大的挑战。为了解决这些问题，我们提出了一种新型的端到端专业网络，该网络由具有深度学习的前列腺分割的密集扩张型空间金字塔池（DDSPP）的编码器-解码器结构组成。首先，DDSPP模块用于提取前列腺MR图像中的多尺度卷积特征，然后使用解码器捕获前列腺的清晰边界。在130幅MR图像上产生了具有竞争力的结果，其关键指标骰子相似系数（DSC）和Hausdorff距离（HD）为0。分别为954和1.752毫米。实验结果表明，该方法具有较高的准确性和鲁棒性。