A Deep Learning Health Data Analysis Approach: Automatic 3D Prostate MR Segmentation with Densely-Connected Volumetric ConvNets
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July
- Issue Date:
- 2018-10-10
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| 08489136.pdf | Published version | 1.74 MB |
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© 2018 IEEE. Automated prostate segmentation in 3D medical images play an important role in many clinical applications, such as diagnosis of prostatitis, prostate cancer and enlarged prostate. However, it is still a challenging task due to the complex background, lacking of clear boundary and various shape and texture between the slices. In this paper, we propose a novel 3D convolutional neural network with densely-connected layers to automatically segment the prostate from Magnetic Resonance(MR) images. Compared with other methods, our method has three compelling advantages. First, our model can effectively detect the prostate region in a volume-to-volume manner by utilizing the 3D convolution rather than the 3D convolution, which can fully exploit both spatial and region information. Second, the proposed network architecture alleviates the vanishing-gradient problem, strengthens the information propagation between layers, overcomes the problem of over-fitting and makes the network deeper by adopting a densely-connected manner. Third, besides the densely-connected manner inside each block, we also adopt the long connections strategy between blocks. We evaluate our proposed model on prostate dataset. The experimental results show that our model achieved significant segmentation results and outperformed other state-of-arts methods.
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