Abstract
Accurate and automatic segmentation of the prostate sub-regions is of great importance for the diagnosis of prostate cancer and quantitative analysis of prostate. By analyzing the characteristics of prostate images, we propose a hybrid attention ensemble framework (HAEF) to automatically segment the central gland (CG) and peripheral zone (PZ) of the prostate from a 3D MR image. The proposed attention bridge module (ABM) in the HAEF helps the Unet to be more robust for cases with large differences in foreground size. In order to deal with low segmentation accuracy of the PZ caused by small proportion of PZ to CG, we gradually increase the proportion of voxels in the region of interest (ROI) in the image through a multi-stage cropping and then introduce self-attention mechanisms in the channel and spatial domain to enhance the multi-level semantic features of the target. Finally, post-processing methods such as ensemble and classification are used to refine the segmentation results. Extensive experiments on the dataset from NCI-ISBI 2013 Challenge demonstrate that the proposed framework can automatically and accurately segment the prostate sub-regions, with a mean DSC of 0.881 for CG and 0.821 for PZ, the 95% HDE of 3.57 mm for CG and 3.72 mm for PZ, and the ASSD of 1.08 mm for CG and 0.96 mm for PZ, and outperforms the state-of-the-art methods in terms of DSC for PZ and average DSC of CG and PZ.
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Acknowledgments
This work was supported by the Science and Technology Innovation Action Plan of Shanghai [grant number: 19511121302], and National Natural Science Foundation of China [grant number: 82072021].
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Qiu, M., Zhang, C., Song, Z. (2021). A Hybrid Attention Ensemble Framework for Zonal Prostate Segmentation. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_51
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