LAUSR

PURPOSE Automatic segmentation of prostate magnetic resonance (MR) images is crucial for the diagnosis, evaluation, and prognosis of prostate diseases (including prostate cancer). In recent years, the mainstream segmentation method for the prostate has been converted to convolutional neural networks. However, owing to the complexity of the tissue structure in MR images and the limitations of existing methods in spatial context modeling, the segmentation performance should be improved further METHODS: : In this study, we proposed a novel 3D pyramid pool Unet (3D PPU-net), which benefits from the pyramid pooling structure embedded in the skip connection and the deep supervision in the up-sampling of the 3D Unet. The parallel skip connection of the conventional 3D Unet network causes low-resolution information to be sent to the feature map repeatedly, resulting in blurred image features. To overcome the shortcomings of the conventional 3D Unet, we merge each decoder layer with the feature map of the same scale as the encoder and the smaller-scale feature map of the pyramid pooling encoder. This skip connection combines the low-level details and high-level semantics at two different levels of feature maps. In addition, pyramid pooling performs multi-faceted feature extraction on each image behind the convolutional layer, and deep supervision learns hierarchical representations from comprehensive aggregated feature maps, which can improve the accuracy of the task. RESULTS Experiments on 3D prostate MR images of 78 patients demonstrated that our results were highly correlated with expert manual segmentation. The average relative volume difference (RVD) and Dice similarity coefficient (DSC) of the prostate volume area were 2.32% and 91.03%, respectively. CONCLUSION Quantitative experiments demonstrate that, compared with other methods, the results of our method are highly consistent with the expert manual segmentation. This article is protected by copyright. All rights reserved.