Acta Optica Sinica, Volume. 41, Issue 18, 1810002(2021)
Liver Tumor Segmentation Based on Dilated Convolution of Stacked Tree Aggregation Structure
In order to overcome the loss of detail information caused by down sampling of traditional liver tumor segmentation networks and extract rich multi-scale information at the same time, this paper proposes an algorithm of liver tumor segmentation based on dilated convolution of stacked tree aggregation structure. First, a residual dense module is proposed in the encoder network. Then, a dilated convolution module of stacked tree aggregation structure is added to the encoder-decoder network, which can effectively eliminate the checkerboard artifacts caused by ordinary dilated convolution and improve the segmentation accuracy. Finally, a weighted loss function is used to solve the problem of the imbalance between the foreground and the background in the image. The experimental results show that the Dice similarity coefficient, pixel accuracy rate and intersection ratio of the algorithm on the computer tomography image data set are 0.8026, 0.7974 and 0.7317, respectively.
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Fei Gao, Bin Yan, Jian Chen, Kai Qiao, Peigang Ning, Dapeng Shi. Liver Tumor Segmentation Based on Dilated Convolution of Stacked Tree Aggregation Structure[J]. Acta Optica Sinica, 2021, 41(18): 1810002
Category: Image Processing
Received: Mar. 4, 2021
Accepted: Apr. 7, 2021
Published Online: Sep. 3, 2021
The Author Email: Gao Fei (gfflyfly@163.com)