Abstract
In the field of computer-aided diagnosis (CAD) and treatment evaluation system on hepatic disease diagnosis, the automatic segmentation of the liver from CT volume is the most basic step. It is a difficult task because the shape of liver could be changed by liver tumor, and the intensity of liver is similar to that of other adjacent tissues. In this paper, we proposed a framework based on the U-net architecture, called dense pyramid network to segment the liver from CT images automatically. The main contribution of our network is that, multiple feature maps from the previous level of hierarchy are combined as the input of each layer in the encoding part. This removes the loss of context information between different layers. The model is trained on practical enhanced CT scans, which are gained from People’s Liberation Army General Hospital (PLA). Experimental results demonstrate that our model can effectively improve the segmentation performance of liver, no matter the different shapes between livers. In the experiment, the Dice score of liver segmentation in the arterial phase, venous phase, and delay phase by dense pyramid network was about 95.97%, 96.22%, and 96.16%, respectively, which shows that our model is more suitable for multi-phase liver segmentation.
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Xu, H. et al. (2020). Automatic Segmentation of Liver CT Image Based on Dense Pyramid Network. In: Li, Q., Leahy, R., Dong, B., Li, X. (eds) Multiscale Multimodal Medical Imaging. MMMI 2019. Lecture Notes in Computer Science(), vol 11977. Springer, Cham. https://doi.org/10.1007/978-3-030-37969-8_2
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DOI: https://doi.org/10.1007/978-3-030-37969-8_2
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