Robust liver vessel extraction using 3D U Net with variant dice loss function
Robust liver vessel extraction using 3D U Net with variant dice loss function
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DOI：
10.1016/j.compbiomed.2018.08.018
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发表时间：
2018-10-01
影响因子：
7.7
通讯作者：
Wang, Guangzhi
中科院分区：
文献类型：
--
作者：
Huang, Qing;Sun, Jinfeng;Wang, Guangzhi
目的：从CT图像中提取肝脏血管是肝脏外科手术规划的基础。肝脏血管结构复杂，分割困难，即使是专家手动标注也包含未标记的血管。本文提出了一种基于深度卷积网络的肝脏血管自动提取方法，并研究了不完整数据标注对分割精度评估的影响。方法：在训练样本少、标注不完整的情况下，选择3D U-Net，使用数据增强技术进行准确的肝脏血管提取。为了处理前景（肝脏血管）和背景（肝脏）类之间的高度不平衡，但也提高分割精度，提出了一种基于骰子系数的变体的损失函数，以增加误分类体素的惩罚。我们包括未标记的肝脏血管提取我们的方法在专家手册注释，与专家的目视检查细化，并比较评估之前和之后的procedure.Results：公共数据集Sliver 07和3Dircadb以及本地临床数据集上进行实验。3Dircadb数据集的平均骰子和灵敏度分别为67.5%和74.3%，前注释refinement，与75.3%和76.7%after refinement.Conclusions：所提出的方法是自动的，准确的和强大的高噪声和不同的血管结构的肝脏血管提取。它可用于肝脏手术规划和新数据集的粗略注释。基于一些基准的评价差异及其细化结果表明，监督学习方法应进一步考虑标注的质量。
Purpose: Liver vessel extraction from CT images is essential in liver surgical planning. Liver vessel segmentation is difficult due to the complex vessel structures, and even expert manual annotations contain unlabeled vessels. This paper presents an automatic liver vessel extraction method using deep convolutional network and studies the impact of incomplete data annotation on segmentation accuracy evaluation.Methods: We select the 3D U-Net and use data augmentation for accurate liver vessel extraction with few training samples and incomplete labeling. To deal with high imbalance between foreground (liver vessel) and background (liver) classes but also increase segmentation accuracy, a loss function based on a variant of the dice coefficient is proposed to increase the penalties for misclassified voxels. We include unlabeled liver vessels extracted by our method in the expert manual annotations, with a specialist's visual inspection for refinement, and compare the evaluations before and after the procedure.Results: Experiments were performed on the public datasets Sliver07 and 3Dircadb as well as local clinical datasets. The average dice and sensitivity for the 3Dircadb dataset were 67.5% and 74.3%, respectively, prior to annotation refinement, as compared with 75.3% and 76.7% after refinement.Conclusions: The proposed method is automatic, accurate and robust for liver vessel extraction with high noise and varied vessel structures. It can be used for liver surgery planning and rough annotation of new datasets. The evaluation difference based on some benchmarks, and their refined results, showed that the quality of annotation should be further considered for supervised learning methods.