Abstract
U-net is one of the common segmentation frameworks in the field of medical semantic segmentation. By utilizing skip-connection and multi-scale feature fusion tricks, U-net shows excellent segmentation performance. However, traditional U-net architectures have a large number of parameters, which may make image processing slow. In this paper, a fast and effective glottis segmentation method based on a lightweight U-net is designed to solve the problem of throat glottis segmentation, which ensures the accuracy of the segmentation while taking into account the processing speed. We simplify the model structure and introduce a self-attention module to improve segmentation performance. Besides, we also propose a hybrid loss for glottis region segmentation. Compared with the mainstream segmentation networks such as U-net, PSPNET, Segnet, and NestedU-net, the total number of model parameters we design is less, and the proposed method can achieve higher dice score, mIoU, FWIoU, and mPA.
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61971368, in part by the Natural Science Foundation of Fujian Province of China No. 2019J01003, in part by the National Natural Science Foundation of China under Grant No. U20A20162, in part by the National Natural Science Foundation of China (grant number 61731012).
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Huang, X., Deng, J., Wang, X., Zhuang, P., Huang, L., Zhao, C. (2022). Automatic Glottis Segmentation Method Based on Lightweight U-net. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_8
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