Liver tumor segmentation method based on MSFCN

Jinquan Hou; Feng Wang; Shanshan Wu; Juanjuan Yang

doi:10.1117/12.2640237

9 May 2022 Liver tumor segmentation method based on MSFCN

Jinquan Hou, Feng Wang, Shanshan Wu, Juanjuan Yang

Proceedings Volume 12252, International Conference on Biometrics, Microelectronic Sensors, and Artificial Intelligence (BMSAI); 122520I (2022) https://doi.org/10.1117/12.2640237
Event: International Conference on Biometrics, Microelectronic Sensors, and Artificial Intelligence (BMSAI), 2022, Guangzhou, China

Abstract

For the convolutional neural network in the process of image segmentation, there are problems such as loss of detailed information and global features of the image, and the segmentation results of the full convolutional neural network in the image segmentation process are not refined and lack spatial consistency. This paper proposes a liver tumor segmentation method based on Multiple Supervised Fully Convolutional Networks (MSFCN). In the framework of a fully convolutional network, a supervised side output layer is added to the convolutional layer to guide multi-scale feature learning, which can better capture the local and global features of the image. Experiments are carried out on the LiTS competition dataset, and the experimental results show that the proposed method can significantly improve the segmentation accuracy.

Citation Download Citation

Jinquan Hou, Feng Wang, Shanshan Wu, and Juanjuan Yang "Liver tumor segmentation method based on MSFCN", Proc. SPIE 12252, International Conference on Biometrics, Microelectronic Sensors, and Artificial Intelligence (BMSAI), 122520I (9 May 2022); https://doi.org/10.1117/12.2640237

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