Abstract
In this paper, we proposed an improved 2D U-Net model integrated squeeze-and-excitation layer for prostate cancer segmentation. The proposed model combined a more complex 2D U-Net model and squeeze-and-excitation technique. The model consisted of an encoder stage and a decoder stage. The encoder stage aims to extract features of the input, which contains CONV blocks, SE layers, and max-pooling layers for improving the feature extraction capability of the model. The decoder aims to map the extracted features to the original image with CONV blocks, SE layers, and upsampling layers. The SE layer is implemented to learn more global and local features. Experiments on the public dataset PROMISE12 have demonstrated that the proposed model could achieve state-of-the-art segmentation performance compared with other traditional methods.
Highlights
Prostate cancer has become a high incidence cancer among men
In order to solve the problems above, in this paper, we proposed a more effective model, which utilizes the U-Net as the backbone of our network, and a squeeze-and-excitation layer is added to every convolution operation to select the emphasize the features which are contributed to the prostate cancer segmentation
Our proposed model refers to the U-Net model and fully convolutional network (FCN), which divide the model into the encoder stage and the decoder stage. e overall structure of our model can be seen in Figure 1. e encoder is used to capture the context in the image, and the decoder is used to enable precise localization
Summary
Prostate cancer has become a high incidence cancer among men. Early medical detection and diagnosis of cancers could substantially improve the cure rate among patients. Radiation therapy which uses medical ionizing radiation to kill cancer cells is a very common procedure to treat prostate cancers [1]. The worst disadvantage of the procedure is that the radiation may damage the cells of surrounding tissue when it kills prostate cancer. For the sake of raising the accuracy of radiation therapy and reducing the side effect in surrounding tissue such as bladder and rectum, more delicate prostate cancer diagnosis and more accurate prostate cancer localization methods are required. Compared with FCN, U-Net is completely symmetrical whose encoder stage and decoder stage are similar while FCN’s decoder stage structure is simpler which only uses one deconvolution operation and no more convolution structures such as U-Net. e second difference is about skip connection, FCN uses summation operation while U-Net uses concatenation operation
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