Abstract
Efficient and accurate segmentation of prostate gland facilitates the prediction of the pathologic stage and treatment response. Recently, deep learning methods have been proposed to tackle this issue. However, the effectiveness of these methods is often limited by inadequate semantic discrimination and spatial context modeling. To address these issues, we propose the Hybrid Discriminative Network (HD-Net), which consists of a 3D segmentation decoder using channel attention block to generate semantically consistent volumetric features and an auxiliary 2D boundary decoder guiding the segmentation network to focus on the semantically discriminative intra-slice features. Meanwhile, we further design the pyramid convolution block and residual refinement block for HD-Net to fully exploit multi-scale spatial contextual information of the prostate gland. In addition, to reduce the information loss in propagation and fully fuse the multi-scale feature maps, we introduce inter-scale dense shortcuts for both decoders. We evaluated our model on the Prostate MR Image Segmentation 2012 (PROMISE12) challenge dataset and achieved a synthetic score of 90.34, setting a new state of the art.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 61771397, in part by the Science and Technology Innovation Committee of Shenzhen Municipality, China, under Grants JCYJ20180306171334997, in part by Synergy Innovation Foundation of the University and Enterprise for Graduate Students in Northwestern Polytechnical University under Grants XQ201911, in part by the Project for Graduate Innovation team of Northwestern Polytechnical University, and in part by the US NIH R01 AG049371 and the US NSF IIS 1836938, DBI 1836866, IIS 1845666, IIS 1852606, IIS 1838627, IIS 1837956.
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Jia, H., Song, Y., Huang, H., Cai, W., Xia, Y. (2019). HD-Net: Hybrid Discriminative Network for Prostate Segmentation in MR Images. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_13
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DOI: https://doi.org/10.1007/978-3-030-32245-8_13
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