Abstract
We proposed a context segmentation method for medical images via two deep networks, aiming at providing segmentation contexts and achieving better segmentation quality. The context in this work means the object labels for segmentation. The key idea of our proposed scheme is to develop mechanisms to elegantly transform object detection labels into the segmentation network structure, so that two deep networks can collaboratively operate with loosely-coupled manner. For achieving this, the scalable data transformation mechanisms between two deep networks need to be invented, including representation of contexts obtained from the first deep network and context importion to the second one. The experimental results reveal that the proposed scheme indeed performs superior segmentation quality.
This work was supported by Ministry of Science and Technology (MOST) of Taiwan under Grants MOST 109-2221-E-006-199, 108-2221-E-034-015-MY2, and 109-2218-E-006-007. This work was financially supported by the “Intelligent Manufacturing Research Center” (iMRC) in NCKU from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.
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Jiang, LY., Kuo, CJ., Tang-Hsuan, O., Hung, MH., Chen, CC. (2021). SE-U-Net: Contextual Segmentation by Loosely Coupled Deep Networks for Medical Imaging Industry. In: Nguyen, N.T., Chittayasothorn, S., Niyato, D., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2021. Lecture Notes in Computer Science(), vol 12672. Springer, Cham. https://doi.org/10.1007/978-3-030-73280-6_54
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