论文标题
基于ODOS滤波器和形状特征的CT图像中的肺裂缝分割
Pulmonary Fissure Segmentation in CT Images Based on ODoS Filter and Shape Features
论文作者
论文摘要
对肺解剖结构的先验知识在肺部疾病的诊断中起着至关重要的作用。在CT图像中，由于各种因素，肺裂缝分割是一个强大的任务。为了应对挑战，提出了一种基于ODOS滤波器的有用方法，并提出了形状特征，用于肺裂缝分割。在这里，我们通过合并方向信息和幅度信息来采用ODOS滤波器，以突出裂缝增强的结构特征，该特征可以有效地区分肺裂缝和碎屑。由肺裂缝以2D空间和平面结构在方向领域的平面结构中显示为线性结构的事实，取向曲率标准和方向分区方案被融合在一起以分离不同方向分区中的绒毛斑块和其他结构，从而可以抑制碎屑的部分。考虑到大小场中的肺裂缝和管状结构之间的形状差，将形状测量方法和3D骨架模型组合在一起，以段肺裂缝以进行切割。将我们的方案应用于从公开可用的LOLA11数据集中获得的55张CT扫描时，中位数F1得分，错误发现率（FDR）和假阴性率（FNR）分别为0.896、0.109和0.100，这表明所呈现的方法具有令人满意的肺部液态Fissry Fissure Fissure fissure fissure Chementation Cartivation。
Priori knowledge of pulmonary anatomy plays a vital role in diagnosis of lung diseases. In CT images, pulmonary fissure segmentation is a formidable mission due to various of factors. To address the challenge, an useful approach based on ODoS filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by merging the orientation information and magnitude information to highlight structure features for fissure enhancement, which can effectively distinguish between pulmonary fissures and clutters. Motivated by the fact that pulmonary fissures appear as linear structures in 2D space and planar structures in 3D space in orientation field, an orientation curvature criterion and an orientation partition scheme are fused to separate fissure patches and other structures in different orientation partition, which can suppress parts of clutters. Considering the shape difference between pulmonary fissures and tubular structures in magnitude field, a shape measure approach and a 3D skeletonization model are combined to segment pulmonary fissures for clutters removal. When applying our scheme to 55 chest CT scans which acquired from a publicly available LOLA11 datasets, the median F1-score, False Discovery Rate (FDR), and False Negative Rate (FNR) respectively are 0.896, 0.109, and 0.100, which indicates that the presented method has a satisfactory pulmonary fissure segmentation performance.
