MAS-UNet: a U-shaped network for prostate segmentation

Prostate cancer is a common disease that seriously endangers the health of middle-aged and elderly men. MRI images are the gold standard for assessing the health status of the prostate region. Segmentation of the prostate region is of great significance for the diagnosis of prostate cancer. In the past, some methods have been used to segment the prostate region, but segmentation accuracy still has room for improvement. This study has proposed a new image segmentation model based on Attention UNet. The model improves Attention UNet by using GN instead of BN, adding dropout to prevent overfitting, introducing the ASPP module, adding channel attention to the attention gate module, and using different channels to output segmentation results of different prostate regions. Finally, we conducted comparative experiments using five existing UNet-based models, and used the dice coefficient as the metric to evaluate the segmentation result. The proposed model achieves dice scores of 0.807 and 0.907 in the transition region and the peripheral region, respectively. The experimental results show that the proposed model is better than other UNet-based models.

Automated summary

Prostate cancer is a common and serious disease in middle-aged and elderly men. MRI images are considered the most accurate method for assessing the prostate region. Although there have been previous methods for segmenting the prostate region, there is still room for improvement in segmentation accuracy. This study proposes a new image segmentation model based on Attention UNet, which improves upon existing models and achieves better results in segmenting different regions of the prostate.