A modified scheme for liver tumor segmentation based on cascaded FCNs
Yuwei Pang, Dong Hu, Min Sun · 2019
With the development of artificial intelligence technology, more and more researchers use deep neural network to solve various difficulties in segmenting liver and liver tumors from abdominal CT images. Recently, researchers propose to use cascaded FCNs strategy to segment both liver and liver tumors automatically. The cascaded FCNs trains the first FCN to segment liver region, and uses the output of the first FCN as the input of the second FCN to train the tumor segmentation network. However, in the cascaded FCNs structure, inaccurate segmentation of liver in the first FCN will directly affect the segmentation of tumors at the edge of liver and the second FCN is not conductive to segment small tumors. To address these issues, this paper proposed a modified scheme based on cascaded FCNs: firstly, we modified the first FCN by adopting variable pooling kernel to provide a better liver region for the later segmentation of tumors. Secondly, we replaced the pooling and convolution layer of the second FCN with dilated convolution to improve global feature extraction of small tumors. We extensively evaluated our method on 3D-IRCADb-01 and 2017 LiTS dataset. The DICE coefficient of tumors reached 85.71% and 82.43% respectively, 3.71% and 3.61% higher than the original cascaded FCNs. Experimental results indicate that our method achieved very competitive performance and outperformed other state-of-the-arts on the liver tumor segmentation accuracy.