Abstract
Medical image segmentation is the premise of many medical image applications including disease diagnosis, anatomy, and radiation therapy. This paper presents a k-Dense-UNet for segmentation of Electron Microscopy (EM) images. Firstly, based on the characteristics of the long skip connection of U-Net and the mechanism of short skip connection of DenseNet, we propose a Dense-UNet by embedding the dense blocks into U-Net, leading to deeper layers for better feature extraction. We experimentally show that Dense-UNet outperforms the popular U-Net. Secondly, we combine Dense-UNet with one of the newest U-Net variants called kU-Net into a network called k-Dense-UNet, which consists of multiple Dense-UNet submodules. Skip connections are added between the adjacent submodules, to pass information efficiently, helping the model to identify fine features. Experimental results on the ISBI 2012 EM dataset show that k-Dense-UNet achieves better performance than U-Net and some of its variants.
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Acknowledgement
The first author would like to thank Yuze Guo for helpful discussions. This work was supported by the Zhi-Yuan Chair Professorship Start-up Grant (WF220103010), and Startup Fund (WF220403029) for Youngman Research, from Shanghai Jiao Tong University.
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Qiang, Z., Tu, S., Xu, L. (2019). A k-Dense-UNet for Biomedical Image Segmentation. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_46
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DOI: https://doi.org/10.1007/978-3-030-36189-1_46
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