基于双输入3D卷积神经网络的胰腺分割算法

doi:10.13229/j.cnki.jdxbgxb.20220141

Abstract

Abstract:

Addressing the issue of difficulty in achieving high accuracy in achieving high accuracy in pancreas segmentation， a dual input 3D network to realize panc-reatic segmentation was proposed. Firstly， the boundary area was highlighted by increasing the context residual information between input slices， and then the attention mechanism was introduced to suppress useless features and increase the expression of effective features， which finally improve the accuracy of pancreatic segmentation. The algorithm was evaluated on NIH pancreas segmentation data set. The experiment-al results show that the performance of the algorithm proposed in this paper is superior to the comparison algorithm.

Key words: computer application, medical image, pancreas segmentation, context residual information, attention mechanism

CLC Number:

TP391

Gui-xia LIU,Yu-xin TIAN,Tao WANG,Ming-rui MA. Pancreas segmentation algorithm based on dual input 3D convolutional neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3565-3572.

Figures/Tables 10

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References 23

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实验序号	架构包含模块	Dice系数平均值/%
1	残差模块	82.84±8.98
2	残差模块+SE模块	83.39±6.74
3	残差模块+上下文残差信息	83.57±5.88
4	残差模块+上下文残差信息+SE模块	84.37±5.14

网络包含模块	视图	平均Dice系数/%	最大Dice系数/%	最小Dice系数/%
残差模块+SE模块	-	83.39±6.74	90.88	53.38
残差模块+SE模块+上下文残差信息	水平面	84.37±5.14	91.65	56.35
残差模块+SE模块+上下文残差信息	矢状面	84.29±5.57	91.21	54.95
残差模块+SE模块+上下文残差信息	冠状面	84.47±5.14	92.07	50.86

方法	Mean Dice/%	Max Dice/%	Min Dice/%	K-CV
文献［8］	81.27±6.27	88.96	50.69	4-CV
文献［9］	81.48±6.23	-	-	5-CV
文献［7］	83.18±4.81	91.03	65.10	4-CV
文献［11］	83.70±5.10	91.00	59.00	4-CV
文献［12］	84.10±4.91	-	-	4-CV
本文	84.37±5.14	91.65	56.35	4-CV

Pancreas segmentation algorithm based on dual input 3D convolutional neural network

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