Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

Xiong Li,Seungik Baek,Lan Peng,Hongbin Tu,Yuanyuan Peng,Hualan Zhong,Zheng Xu

doi:10.1155/2021/5588629

Abstract

In computed tomography (CT) images, pulmonary lobe segmentation is an arduous task due to its complex structures. To remedy the problem, we introduce a new framework based on lung anatomy knowledge for lung lobe segmentation. Firstly, the priori knowledge of lung anatomy is used to identify the fissure region of interest. Then, an oriented derivative of stick filter is applied to isolate plate-like structures from clutters for lobar fissure verification. Finally, a surface fitting model is employed to complete the incomplete fissure surface for lung lobe segmentation. Compared with manually segmented fissure references, the designed approach obtained a high median F1-score of 0.8865 in the left lung and obtained a high median F1-score of 0.9200 in the right lung. The average percentages of the segmented lung lobes in the lung lobe ground truth are 0.960, 0.989, 0.973, 0.920, and 0.985 for the left upper, left lower, right upper, right middle, and right lower lobes, respectively. The perfect performance of the proposed scheme is tested by visual inspection and quantitative evaluation.

Highlights

In medical practice, recognition of lung lobes is useful for clinical diagnosis and lung disease assessment [1]
A schematic diagram is shown in Figure 1; the human lungs are composed of five lobes. e right lung is subdivided into the right upper lobe (RUL), the right middle lobe (RML), and the right lower lobe (RLL), separated by the right oblique fissure and the right horizontal fissure, whereas the left lung is subdivided into the left upper lobe (LUL) and the left lower lobe (LLL), separated by the left oblique fissure
Quantitative Evaluation. e proposed method is validated on 15 computed tomography (CT) examinations in Figure 15. e box plots of indices (F1, false discovery rate (FDR), and false negative rate (FNR)) corresponding to the proposed method (p), oriented derivative of stick (ODoS), and derivative of stick (DoS) (d) filtering scheme are drawn next to each other

Summary

Introduction

Recognition of lung lobes is useful for clinical diagnosis and lung disease assessment [1]. Accurate segmentation of lung lobes is urgently needed. Lung lobe segmentation is extremely important in surgical treatment of lung diseases [3, 4]. Manual segmentation of lung lobes is impracticable in CT images due to the large number of images. Erefore, automatic segmentation methods for lung lobes are necessary. A schematic diagram is shown in Figure 1; the human lungs are composed of five lobes. Lung lobes are served by separate bronchial trees and their corresponding pulmonary arteries and are anatomically independent. Lung lobe segmentation is an arduous task due to complex lung anatomy

Methods

Results

Discussion

Conclusion