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Pulmonary fissure segmentation in CT images based on ODoS filter and shape features

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Abstract

Pulmonary fissure segmentation in computed tomography (CT) images can be treated as important ancillary information in the diagnosis and treatment of pulmonary diseases, yet it poses a nontrivial uncertainty for the segmentation task due to complex structures such as indistinguishable pulmonary vessels, blurring pulmonary fissures and unpredictable pathological deformation. To address these challenges, a useful approach based on an oriented derivative of stick (ODoS) filter and shape features is presented for pulmonary fissure segmentation. Here, we adopt an ODoS filter by fusing its orientation and magnitude information to highlight structural features for fissure enhancement, which can effectively distinguish between pulmonary fissures and undesirable clutter. Motivated by the fact that pulmonary fissures appear as linear structures in 2D space and planar structures in 3D space in the orientation field, an orientation curvature criterion and an orientation partition scheme are fused to separate fissure patches and other structures in different orientation partitions, which is expected to achieve more complete fissure detection and suppress other structures. Considering the shape difference between pulmonary fissures and tubular structures in the magnitude field, a shape measurement approach and a 3D skeletonization model are combined to remove clutter for pulmonary fissure segmentation. When applying our scheme to 55 chest CT scans acquired from publicly available LOLA11 datasets, the median F1-score, false discovery rate (FDR), and false negative rate (FNR) were 0.90, 0.11, and 0.10, respectively, which indicates that our scheme has satisfactory pulmonary fissure segmentation performance.

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Data Availability

The LOLA11 dataset can be download in the website https://lola11.grand-challenge.org/.

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Acknowledgements

This research was supported by the Jiangxi Provincial Natural Science Foundation (nos. 20212BAB202007, 20202BAB212004, 20212BAB211009, 20204BCJL23035, 20192ACB21004, 20181BAB202017), the Hunan Provincial Natural Science Foundation (no. 2021JJ30165), the Hunan Special Funds for the Construction of Innovative Province(Huxiang High-level Talent Gathering Project-Innovative talents) (no. 2019RS1072), the Educational Science Research Project of China Institute of communications Education (no. JTYB20-33), the Scientific and Technological Research Project of Education Department in Jiangxi Province (nos. GJJ190356, GJJ210645) and the Science and Technology project of Changsha City (no. kq2001014).

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Authors and Affiliations

  1. School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330000, People’s Republic of China

    Yuanyuan Peng, Pengpeng Luan & Hongbin Tu

  2. School of Computer Science, Northwestern Polytechnical University, Xi’An, 710000, People’s Republic of China

    Yuanyuan Peng

  3. School of Software, East China Jiaotong University, NanChang, 330000, People’s Republic of China

    Xiong Li

  4. College of Biology, Hunan University, Changsha, 410000, People’s Republic of China

    Ping Zhou

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Contributions

Conceptualization, Yuanyuan Peng and Hongbin Tu; methodology, Yuanyuan Peng and Pengpeng Luan; validation, Yuanyuan Peng and Xiong Li; formal analysis, Xiong Li; data curation, Yuanyuan Peng and Xiong Li; writing original draft preparation, Yuanyuan Peng; funding acquisition, Yuanyuan Peng, Hongbin Tu and Xiong Li; paper modification, Yuanyuan Peng and Ping Zhou. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yuanyuan Peng.

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Peng, Y., Luan, P., Tu, H. et al. Pulmonary fissure segmentation in CT images based on ODoS filter and shape features. Multimed Tools Appl 82, 34959–34980 (2023). https://doi.org/10.1007/s11042-023-14931-y

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