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Accurate segmentation of prostate magnetic resonance (MR) images provides sufficient information for the diagnosis and treatment of prostate cancer. However, the automatic segmentation of prostate based on 3D MR images faces some challenges. First, the prostate differs from other anatomical structures in that its gland lacks clear boundaries. Moreover, there are large differences in the background texture, shape, and size of different samples of prostate MR images, which makes segmentation accuracy difficult to improve. To address this issue, we propose an automatic segmentation model called the Shape Constraint U-Net (SCU-Net) for prostate MR images. The network focuses on the segmentation of the prostate boundary region by introducing a shape constraint stream based on a parallel encoder–decoder structure that can handle different shape and texture information. Specifically, the shape constraint stream is composed of a multi-level boundary attention module that can process boundary-related information using advanced activation features in the regular segmentation stream. Finally, the multi-scale context information extracted from the regular segmentation stream is fused with the multi-level boundary information obtained from the shape constraint stream to generate a segmentation prediction map. Owing to the additional shape constraints, the network substantially improves prostate boundary region segmentation. We evaluated our method on two different real clinical MR prostate datasets. The experimental results demonstrate that SCU-Net achieves state-of-the-art prostate segmentation accuracy, especially in boundary-region voxel prediction. Further analysis indicates that the proposed shape constraint stream can be used to improve the boundary voxel prediction performance of other segmentation networks.
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