Abstract
In recent years, 3D segmentation of the pancreas has received a lot of attention from researchers because of its importance for clinical diagnosis and treatment. However, there are many problems with 3D pancreatic segmentation: 1) because the shape of the pancreas is not regular enough compared to other organs in the abdomen, it has a relatively small shape and there is also an excessive background of interference, resulting in inaccurate segmentation results for the pancreas; 2) one of the main drawbacks of 3D convolutional neural networks for segmentation is the excessive memory occupation, which requires censoring of the network structure to fit a given memory budget. To address the above issues, this paper proposes a new coarse-to-fine method based on convolutional neural networks (CNNs). In the first stage, the segmentation is trained to obtain candidate regions. In the second stage, the approximate location of the pancreas is obtained after the first stage, and then the pancreas is finely segmented in this approximate location. The convolutional neural network used in this paper is a modified 3DUnet network, which is improved to require less memory and higher segmentation accuracy compared to the traditional 3DUnet network. This segmentation method requires a less demanding experimental environment than other algorithms, and can also improve accuracy by eliminating a large amount of irrelevant background interference. The combination of our proposed network structure and the two-stage segmentation method achieves advanced performance.
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Acknowledgment
This work was supported by Zhenjiang Key Deprogram “Fire Early Warning Technology Based on Multimodal Data Analysis” (SH2020011) Jiangsu Emergency Management Science and Technology Project “Research on Very Early Warning of Fire Based on Multi-modal Data Analysis and Multi-Intelligent Body Technology” (YJGL-TG-2020–8).
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Wang, W. et al. (2021). Efficient 3D Pancreas Segmentation Using Two-Stage 3D Convolutional Neural Networks. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_17
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