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DV-Net: Accurate liver vessel segmentation via dense connection model with D-BCE loss function
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2021-09-10 , DOI: 10.1016/j.knosys.2021.107471
Jun Su 1 , Zhe Liu 1 , Jing Zhang 2 , Victor S. Sheng 3 , Yuqing Song 1 , Yan Zhu 4 , Yi Liu 1
Affiliation  

Recently, liver vessel segmentation has aroused widespread interest in medical image analysis. Accurately extracting blood vessels from livers is a difficult task due to their complex vessel structures and image noises. To make a neural network better adapt to this complexity, a deeper neural network is required to fit this nonlinear transformation. In particular, accurate segmentation of small blood vessels is always a challenge, since onefold down-sampling usually causes the loss of information. In this study, we introduce a dense block structure into the V-net to construct a new Dense V-Net (DV-Net) and use data augmentation to segment liver vessels from abdominal CT volumes with a few training samples. In addition, we propose a dual-branch dense connection down-sampling strategy (DCDS) to better capture vascular features and a D-BCE loss function to maximize the utilization of image resources. The proposed DV-Net structure is more powerful in the discrimination of vessel and non-vessel areas. We extensively evaluated the proposed method on the datasets of 3Dircadb and MICCAI 2018 Medical Segmentation Decalthon (MSD) Challenge. Experimental results show that the proposed DV-Net significantly improves the average segmentation Dice score. The average Dice score and sensitivity on 3Dircadb were (75.46%) and (76.93%), respectively, which are better than those of existing methods.



中文翻译:

DV-Net:通过具有 D-BCE 损失函数的密集连接模型准确分割肝血管

最近,肝血管分割引起了医学图像分析的广泛兴趣。由于肝脏复杂的血管结构和图像噪声,从肝脏中准确提取血管是一项艰巨的任务。为了让神经网络更好地适应这种复杂性,需要一个更深的神经网络来适应这种非线性变换。特别是小血管的精确分割始终是一个挑战,因为单倍下采样通常会导致信息丢失。在这项研究中,我们在 V-net 中引入了一个密集块结构来构建一个新的密集 V-Net (DV-Net),并使用数据增强从腹部 CT 体积中用少量训练样本分割肝血管。此外,我们提出了一种双分支密集连接下采样策略 (DCDS) 以更好地捕获血管特征和 D-BCE 损失函数以最大限度地利用图像资源。所提出的 DV-Net 结构在区分船只和非船只区域方面更强大。我们在 3Dircadb 和 MICCAI 2018 Medical Segmentation Decalthon (MSD) Challenge 的数据集上广泛评估了所提出的方法。实验结果表明,所提出的 DV-Net 显着提高了平均分割 Dice 得分。3Dircadb 上的平均 Dice 得分和灵敏度分别为(75.46%)和(76.93%),优于现有方法。我们在 3Dircadb 和 MICCAI 2018 Medical Segmentation Decalthon (MSD) Challenge 的数据集上广泛评估了所提出的方法。实验结果表明,所提出的 DV-Net 显着提高了平均分割 Dice 得分。3Dircadb 上的平均 Dice 得分和灵敏度分别为(75.46%)和(76.93%),优于现有方法。我们在 3Dircadb 和 MICCAI 2018 Medical Segmentation Decalthon (MSD) Challenge 的数据集上广泛评估了所提出的方法。实验结果表明,所提出的 DV-Net 显着提高了平均分割 Dice 得分。3Dircadb 上的平均 Dice 得分和灵敏度分别为(75.46%)和(76.93%),优于现有方法。

更新日期:2021-09-15
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