Cascade Dense-Unet for Prostate Segmentation in MR Images

Automatic prostate segmentation from magnetic resonance images can assist in diagnosis and radiological planning. The extensive clinical application of this task has attracted the attention of researchers. However, due to noise, blurred boundaries and scale variation, it is very challenging to segment prostate from magnetic resonance images. We propose a cascade method for prostate segmentation. The model consists of two stage. In the first stage, a dense-unet model are used to obtain the initial segmentation results. In the second stage, the segmentation result of the first stage is used as prior knowledge, and another dense-unet is used to obtain more accurate segmentation results. The experimental results show that the proposed method can obtain more accurate segmentation results.

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