Abstract
Nucleus segmentation is an important task in medical image analysis. However, machine learning models cannot perform well because there are large amount of clusters of crowded nuclei. To handle this problem, existing approaches typically resort to sophisticated hand-crafted post-processing strategies; therefore, they are vulnerable to the variation of post-processing hyper-parameters. Accordingly, in this paper, we devise a Boundary-assisted Region Proposal Network (BRP-Net) that achieves robust instance-level nucleus segmentation. First, we propose a novel Task-aware Feature Encoding (TAFE) network that efficiently extracts respective high-quality features for semantic segmentation and instance boundary detection tasks. This is achieved by carefully considering the correlation and differences between the two tasks. Second, coarse nucleus proposals are generated based on the predictions of the above two tasks. Third, these proposals are fed into instance segmentation networks for more accurate prediction. Experimental results demonstrate that the performance of BRP-Net is robust to the variation of post-processing hyper-parameters. Furthermore, BRP-Net achieves state-of-the-art performances on both the Kumar and CPM17 datasets. The code of BRP-Net will be released at https://github.com/csccsccsccsc/brpnet.
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Notes
- 1.
The pretrained model can be downloaded from https://download.pytorch.org/models/densenet121-a639ec97.pth.
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Acknowledgement
Changxing Ding is the corresponding author. This work was supported by NSF of China under Grant 61702193, the Science and Technology Program of Guangzhou under Grant 201804010272, the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant 2017ZT07X183, the Fundamental Research Funds for the Central Universities of China under Grant 2019JQ01, the Guangzhou Key Laboratory of Body Data Science under Grant 201605030011, and the Australian Research Council Project FL-170100117.
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Chen, S., Ding, C., Tao, D. (2020). Boundary-Assisted Region Proposal Networks for Nucleus Segmentation. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_27
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