## 1 Introduction  
The human lung is divided into five lobes by the lung fissures and lung boundaries. The lung lobe is a relatively independent unit with regards to anatomical structure and function. Knowledge of the distribution of lung diseases among the lung lobes is important for treatment planning [1]. Moreover, lung lobe segmentation is indispensable for quantitative evaluation of lung diseases. Manual lung lobe segmentation suffers from subjective bias and variance. Therefore, there is a need to develop an automatic lung lobe segmentation method with high robustness and accuracy for clinical image data analysis.  
Presently, a number of works have been devoted to lung lobe segmentation, which can be divided into two categories: the supervised machine learning method and the unsupervised traditional medical image processing method according to [2]. The traditional segmentation methods can be further classified into two categories: the algorithm based on the fusion of anatomical structure information and the method without the aid of anatomical structure.  
Some machine learning methods have been used to segment lung lobes in recent years. [PERSON] et al. used progressive holistically nested neural networks to predict lung lobe boundaries in 2D slices, and then combined them with the 3D random walker to achieve lung lobe segmentation [3]. [PERSON] et al. proposed a fast and reliable progressive dense V-network [4]. In order to prevent over fitting, [PERSON] et al. integrated several regularization methods on the basis of a residual network [5]. These methods based on deep learning can improve the efficiency of lung lobe segmentation to a certain extent, but they need a large amount of training data to be manually labeled by experts and high-end Graphics Processing Unit (GPU) equipment for training.  
In the unsupervised traditional lung lobe segmentation algorithms, there are two main steps including lung segmentation and lung fissure detection. The former has been fully studied and is a stable and reliable algorithm [6; 7; 8]. On the other hand, automatic detection of pulmonary fissures is still challenging. There are some methods that use the local and global knowledge of lung anatomical structures to locate the lung fissure area, such as using airway and vessel information [9; 10; 11; 12], which is based on the assumption that there are no blood vessels penetrating the pulmonary fissure and the airway tree can be divided into branch trees belonging to five lung lobes. These methods require segmentation of the airway and vessel tree, so the algorithm is time-consuming, and its performance depends on segmentation accuracy. For pathological data, once a segmentation error occurs, detection of the pulmonary fissure cannot be carried out successfully.  
Because the right lung has two types of fissures, the horizontal fissure and the oblique fissure, it is necessary to classify them before segmenting the lobes correctly. In this paper, a lung lobe segmentation method based on lung fissure classification is proposed. In order to optimize the calculation and save space, we represent the pulmonary fissure surface extracted by a directional derivative of plate (DDoP) filter [13] as the point cloud data structure. Based on this, we can calculate the normal and curvature differences of the pulmonary fissure surface with high accuracy, and then realize the pulmonary fissure classification using a region growing approach. For missing pulmonary fissures after classification, they are interpolated and expanded by the multistage spline surface fitting method, and then combined fitted fissures with a lung mask to realize lung lobe segmentation. This method does not rely on anatomical structure information such as airways or vessels, and experiments show that it has higher reliability for detecting mild or moderate lesions on lung CT images.