[1]朱诗生,王慧娟,李淳鑫.基于深度学习和模型集成的肺结节分割方法[J].计算机技术与发展,2023,33(02):208-213.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 031]
　ZHU Shi-sheng,WANG Hui-juan,LI Chun-xin.Pulmonary Nodule Segmentation Method Based on Deep Learning and Model Integration[J].,2023,33(02):208-213.[doi:10. 3969 / j. issn. 1673-629X. 2023. 02. 031]
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基于深度学习和模型集成的肺结节分割方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年02期
页码:
208-213
栏目:
新型计算应用系统
出版日期:
2023-02-10

文章信息/Info

Title:
Pulmonary Nodule Segmentation Method Based on Deep Learning and Model Integration
文章编号:
1673-629X(2023)02-0208-06
作者:
朱诗生; 王慧娟; 李淳鑫
汕头大学 计算机系,广东 汕头 515063
Author(s):
ZHU Shi-sheng; WANG Hui-juan; LI Chun-xin
Department of Computer Science,Shantou University,Shantou 515063,China
关键词:
深度学习; 肺结节; 分割; 全连接条件随机场; 集成学习
Keywords:
deep learning; pulmonary nodule; segmentation; fully-connected conditional random field; ensemble learning
分类号:
TP391. 4;R814. 42
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 02. 031
摘要:
针对 CT 图像中肺结节所占的比率比较小、特征复杂及分割精准度不高的难题,提出了一种基于深度学习和模型集成的肺结节分割方法。 该方法在数据采样上,为解决胸部 CT 图像中存在的类别不平衡问题和避免模型对图像中肺结节位置的过度学习,提出了一种新的随机方向采样方式。 首先,将采样图截成 64×64 的大小;然后,在对 CT 图像进行分割预测时采用步长为 32 的遍历预测叠加方式,来避免肺结节被遗漏的现象,以提升模型性能;在分割结果上,提出在卷积网络后连接条件随机场,通过结合肺结节相邻像素点的信息来优化分割的结果;在此基础上,创新性地将多种深度学习模型(U-Net、LinkNet 和 SegNet) 的肺结节分割结果进行集成,从而进一步提升肺结节分割的精准度。 在 LIDC-IDRI 肺结节公开数据库上的实验验证结果表明,该方法可以更有效地提高肺结节分割的精准度,更有助于提升医生对肺癌的诊治水平。
Abstract:
In view of the problems that small proportion of pulmonary nodules in CT images,complex features and low segmentationaccuracy,a pulmonary nodule segmentation method based on deep learning and model integration is proposed. In terms of data sampling,a new random direction sampling method is proposed by considering the class imbalance in chest CT images and avoid over-learning thelocation of pulmonary nodule by the model. Firstly,the sampling figure is cut into a size of 64×64. Then,in the segmentation predictionof CT images,the method of traversal prediction superposition with a step size of 32 is adopted to avoid the phenomenon of missing pulmonary nodules,so as to improve model performance. In terms of segmentation results,we propose to optimize the segmentation resultsby means of the fully - connected conditional random field. On this basis,the pulmonary nodule segmentation results of multiple deeplearning models ( U-Net,LinkNet and SegNet) are innovatively integrated,so as to further improve the accuracy of pulmonary nodulesegmentation. The experimental verification results of LIDC-IDRI pulmonary nodule open database show that the proposed method canimprove the accuracy of pulmonary nodule segmentation more effectively,which is more conductive to improving doctors爷 diagnosis andtreatment level of lung cancer.

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更新日期/Last Update: 2023-02-10