Abstract
Deep neural networks have shown great potential in medical image segmentation fields. Most of the current methods are based on UNet, which generates long-range dependence step by step by stacking a large number of local operations. However, global information cannot be effectively aggregated using only local operations. The nonlocal module is an effective method for obtaining global information, but the nonlocal block is always criticized for its exorbitant computation and GPU consumption. For the purpose of solving this problem, a faster nonlocal UNet (FN-UNet) is proposed for obtaining the long-range dependency of biomedical images through a more effective and efficient method. Inspired by the recently introduced nonlocal U-Nets and asymmetric nonlocal neural networks, we integrate the asymmetric fusion nonlocal block (AFNB) and asymmetric pyramid nonlocal block (APNB) into the proposed network. Additionally, thorough experiments are performed on the cell segmentation dataset of the ISBI Challenge. The results show that compared with the typical nonlocal UNet network, the proposed network can obtain better results on the basis of occupying less GPU memory and computation.
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Acknowledgment
This work is supported by the Innovation Capacity Construction Project of Jilin Province Development and Reform Commission (2019C053-3), the Science & Technology Development Project of Jilin Province, China (20190302117GX) and the National Key Research and Development Program of China (No. 2020YFA0714103).
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Lin, X., Wang, S. (2021). Faster Nonlocal UNet for Cell Segmentation in Microscopy Images. In: Qiu, H., Zhang, C., Fei, Z., Qiu, M., Kung, SY. (eds) Knowledge Science, Engineering and Management. KSEM 2021. Lecture Notes in Computer Science(), vol 12817. Springer, Cham. https://doi.org/10.1007/978-3-030-82153-1_38
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DOI: https://doi.org/10.1007/978-3-030-82153-1_38
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