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Lung Lobe Segmentation Based on Lung Fissure Surface Classification

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Lung Lobe Segmentation Based on Lung Fissure Surface Classification algorithms Article Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach Xin Chen 1, Hong Zhao 2 and Ping Zhou 3,* 1 College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; [email protected] 2 College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410003, China; [email protected] 3 College of Biology, Hunan University, Changsha 410082, China * Correspondence: [email protected] Received: 2 September 2020; Accepted: 13 October 2020; Published: 15 October 2020 Abstract: In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures. Keywords: lung lobe segmentation; lung fissure classification; point cloud; region growing; normal vector and curvature; principal component analysis; multistage spline fitting 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. Algorithms 2020, 13, 263; doi:10.3390/a13100263 www.mdpi.com/journal/algorithms Algorithms 2020, 13, 263 2 of 12 Some machine learning methods have been used to segment lung lobes in recent years. George et al. usedAlgorithms progressive 2020, 13 holistically, x FOR PEER REVIEW nested neural networks to predict lung lobe boundaries in22D of 12 slices, and then combinedSome machine them learning with the methods 3D random have been walker used to to achieve segment lung lung lobe lobes segmentation in recent years. [3]. George Imran et al. proposedet al. used a fast progressive and reliable holistically progressive nested denseneural V-networknetworks to [predict4]. In lung order lobe to boundaries prevent over in 2D fitting, Ferreiraslices, et and al. then integrated combined several them with regularization the 3D random methods walker to on achieve the basis lung of lobe a residualsegmentation network [3]. [5]. TheseImran methods et al. proposed based on a deepfast and learning reliable can progressiv improvee dense the eV-networkfficiency of[4]. lung In order lobe to segmentation prevent over to a certainfitting, extent, Ferreira but et they al. integrated need a large several amount regularization of training methods data toon bethemanually basis of a residual labeled network by experts [5]. and high-endThese Graphics methods Processingbased on deep Unit learning (GPU) can equipment improve th fore training.efficiency of lung lobe segmentation to a certainIn the extent, unsupervised but they traditionalneed a large lungamount lobe of trai segmentationning data to algorithms,be manually labeled there are by experts two main and steps includinghigh-end lung Graphics segmentation Processing and Unit lung (GPU) fissure equipment detection. for training. The former has been fully studied and is a In the unsupervised traditional lung lobe segmentation algorithms, there are two main steps stable and reliable algorithm [6–8]. On the other hand, automatic detection of pulmonary fissures is including lung segmentation and lung fissure detection. The former has been fully studied and is a still challenging.stable and reliable There algorithm are some [6–8]. methods On the that other use hand, the localautomatic and globaldetection knowledge of pulmonary of lung fissures anatomical is structuresstill challenging. to locate the There lung are fissure some methods area, such that as use using the local airway and andglobal vessel knowledge information of lung [anatomical9–12], which is basedstructures on the assumption to locate the lung that therefissure are area, no such blood as using vessels airway penetrating and vessel the information pulmonary [9–12], fissure which and the airwayis based tree canon the be assumption divided into that branchthere are trees no blood belonging vessels penetrating to five lung the lobes. pulmonary These fissure methods and the require segmentationairway tree of can the be airway divided and into vessel branch tree, trees so thebelonging algorithm to five is time-consuming,lung lobes. These methods and its performancerequire dependssegmentation on segmentation of the airway accuracy. and ve Forssel pathological tree, so the algorithm data, once is time-consuming, a segmentation errorand its occurs, performance detection of the pulmonarydepends on fissure segmentation cannot beaccuracy. carried For out patholog successfully.ical 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 Because the right lung has two types of fissures, the horizontal fissure and the oblique fissure, it necessary to classify them before segmenting the lobes correctly. In this paper, a lung lobe segmentation is necessary to classify them before segmenting the lobes correctly. In this paper, a lung lobe methodsegmentation based on lungmethod fissure based classification on lung fissure is proposed. classification In orderis prop toosed. optimize In order the calculationto optimize andthe save space,calculation we represent and save the pulmonary space, we represent fissure surface the pulm extractedonary fissure by a directionalsurface extracted derivative by a ofdirectional plate (DDoP) filterderivative [13] as the of pointplate (DDoP) cloud data filter structure.[13] as the point Based cloud on this, data westructure. can calculate Based on the this, normal we can and calculate curvature differencesthe normal of the and pulmonary curvature differences fissure surface of the with pulmon highary accuracy, fissure surface and then with realize high accuracy, the pulmonary and then fissure classificationrealize the using pulmonary a region fissure growing classification approach. using a For region missing growing pulmonary approach.fissures For missing after pulmonary classification, theyfissures are interpolated after classification, and expanded they byare theinterpolated multistage and spline expanded surface by fitting the multistage method, andspline then surface combined fittedfitting fissures method, with and a lung then combined mask to realizefitted fissures lung with lobe a segmentation. lung mask to realize This lung method lobe segmentation. does not rely on This method does not rely on anatomical structure information such as airways or vessels, and anatomical structure information such as airways or vessels, and experiments show that it has higher experiments show that it has higher reliability for detecting mild or moderate lesions on lung CT reliability for detecting mild or moderate lesions on lung CT images. images. 2. Lung Lobe Segmentation Algorithm 2. Lung Lobe Segmentation Algorithm FigureFigure1 shows 1 shows the the basic basic anatomical anatomical structures structures ofof the human lung. lung. Based Based on onthis this anatomical anatomical information,information, we we propose propose aa three—step lung lung lobe lobe segmentation segmentation framework, framework, which includes which (1) includes lung (1) lungsegmentation, segmentation, (2) (2) lung lung fissure fissure classification, classification, and and(3) lung (3) lung fissure fissure surface surface fitting fitting and lung and lunglobe lobe segmentation.segmentation. Among Among them, them, we we adopt adopt aa relativelyrelatively mature mature method method for for lung lung
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