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Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge

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  • Yuanyuan Peng
  • Hualan Zhong
  • Zheng Xu
  • Hongbin Tu
  • Xiong Li
  • Lan Peng

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 F 1 -score of 0.8865 in the left lung and obtained a high median F 1 -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.

Suggested Citation

  • Yuanyuan Peng & Hualan Zhong & Zheng Xu & Hongbin Tu & Xiong Li & Lan Peng, 2021. "Pulmonary Lobe Segmentation in CT Images Based on Lung Anatomy Knowledge," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, April.
    • Handle: RePEc:hin:jnlmpe:5588629
    DOI: 10.1155/2021/5588629
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