Abstract
Biomedical image segmentation plays an important role in automatic disease diagnosis. However, some particular biomedical images have blurred object boundaries, and may contain noises due to the limited performance of imaging device. This issue will highly affects segmentation performance, and will become even severer when images have to be resized to lower resolution on a machine with limited memory. To address this, we propose a guide-based model, called G-MNet, which seeks to exploit edge information from guided map to guide the corresponding lower resolution outputs. The guided map is generated from multi-scale input to provide a better guidance. In these ways, the segmentation model will be more robust to noises and blurred object boundaries. Extensive experiments on two biomedical image datasets demonstrate the effectiveness of the proposed method.
This work was done when S. Zhang and Y. Yan are interns at CVTE Research.
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Acknowledgements
This work was supported by National Natural Science Foundation of China (NSFC) 61602185 and 61876208, Guangdong Introducing Innovative and Enterpreneurial Teams 2017ZT07X183, and Guangdong Provincial Scientific and Technological Fund 2018B010107001, 2017B090901008 and 2018B010108002, and Pearl River S&T Nova Program of Guangzhou 201806010081, and CCF-Tencent Open Research Fund RAGR20190103, and National Key R&D Program of China #2017YFC0112404.
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Zhang, S. et al. (2019). Guided M-Net for High-Resolution Biomedical Image Segmentation with Weak Boundaries. In: Fu, H., Garvin, M., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2019. Lecture Notes in Computer Science(), vol 11855. Springer, Cham. https://doi.org/10.1007/978-3-030-32956-3_6
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DOI: https://doi.org/10.1007/978-3-030-32956-3_6
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