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

摘要/Abstract

摘要：

针对胰腺分割难以获得较高准确率的问题，本文提出了一种基于双输入3D卷积神经网络的胰腺分割算法。首先，通过增加输入切片间上下文残差信息突出边界区域；然后，引入注意力机制抑制无用特征、增加有效特征的表达，提高了胰腺的分割准确率；最后，将该算法在NIH胰腺分割数据集上进行评估。实验结果表明，本文算法性能优先于对比的主流算法。

关键词: 计算机应用, 医学图像, 胰腺分割, 上下文残差信息, 注意力机制

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

中图分类号:

TP391

刘桂霞,田郁欣,王涛,马明睿. 基于双输入3D卷积神经网络的胰腺分割算法[J]. 吉林大学学报(工学版), 2023, 53(12): 3565-3572.

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.

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参考文献 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