结合肺先验与协同深监督的肿瘤自动分割
Automatic segmentation of tumors combining lung prior and synergetic deep supervision
王兵 1巨梦仪 2杨颖 3张欣 4翟俊海1
作者信息
- 1. 河北省机器学习与计算智能重点实验室,河北保定 071002;河北大学数学与信息科学学院,河北保定 071002
- 2. 河北大学数学与信息科学学院,河北保定 071002
- 3. 河北大学附属医院放射科,河北保定 071000
- 4. 河北大学电子信息工程学院,河北保定 071002
- 折叠
摘要
计算机断层扫描图像中复杂肺肿瘤(CLT)的自动分割面临2个挑战:1)肿瘤与邻近组织之间的类间不区分;2)肿瘤内的类内不一致性.为了解决这2个问题,提出将肺肿瘤与肺之间关系的语义上下文先验纳入分割模型中,以便于模型学习到语义上下文特征,并从宏观角度重新思考CLT的分割.利用信息熵对肺形状的解剖先验进行建模.在三分类的U-Net网络中嵌入提出的新型注意模块,从而通过特定领域的知识来指导训练过程.另外,设计了一个可以获得肿瘤边界结构图以及保持肿瘤内部特征一致性的边界增强辅助网络.在此基础上,开发了 一个协同深度监督网络框架(CLT-ASegNet),该框架利用混合多尺度语义特征融合进一步提高了模型的判别能力和收敛速度.CLT-ASegNet在CLTCTI分割数据集和Lung16数据集上进行了评估.实验结果表明,所提出的CLT-ASegNet可以有效分割肺肿瘤.
Abstract
There are two challenges in automatic segmentation of complex lung tumors(CLT)on computed tomography(CT)images:1)The class indistinction between tumors and adjacent tissues;2)Intra-class inconsistencies within tumors.In order to solve these two challenges,the semantic context prior of the relationship between lung tumor and lung is proposed to be incorporated into the segmentation model,so that the model can learn the semantic context features,and the segmentation of CLT can be reconsidered from a macro perspective.The anatomical prior of lung shape is modeled using information entropy.The proposed novel attention module is embedded in the three-classified U-Net network,so as to guide the training process through domain-specific knowledge.In addition,a boundary enhancement auxiliary network was designed to obtain tumor boundary structure and maintain the consistency of tumor internal features.On this basis,a collaborative deep supervision network framework(CLT-ASegNet)was developed,which further improved the discriminant ability and convergence speed of the model by using hybrid multi-scale semantic feature fusion.CLT-ASegNet was evaluated on CLTCT1 segmentation datasets and Lung16 datasets.The experimental results show that the proposed CLT-ASegNet can effectively segment lung tumors.
关键词
注意力机制/复杂肺肿瘤分割/语义上下文先验/协同深度监督Key words
attention mechanism/complex lung tumor segmentation/semantic context prior/synergetic deep supervision引用本文复制引用
基金项目
河北省自然科学基金资助项目(F2021201020)
河北省自然科学基金青年科学基金资助项目(F2024502006)
出版年
2024
NETL
NSTL
万方数据