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Multi-scale dense selective network based on border modeling for lung nodule segmentation

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Accurate quantification of pulmonary nodules helps physicians to accurately diagnose and treat lung cancer. We try to improve the segmentation efficiency of irregular nodules while maintaining the segmentation accuracy of simple types of nodules.

Methods

In this paper, we obtain the unique edge part of pulmonary nodules and process it as a single branch stream, i.e., border stream, to explicitly model the nodule edge information. We propose a multi-scale dense selective network based on border modeling (BorDenNet). Its overall framework consists of a dual-branch encoder–decoder, which achieves parallel processing of classical image stream and border stream. We design a dense attention module to facilitate a strongly coupled status of feature images to focus on key regions of pulmonary nodules. Then, during the process of model decoding, the multi-scale selective attention module is proposed to establish long-range correlation relationships between different scale features, which further achieves finer feature discrimination and spatial recovery. We introduce border context enhancement module to mutually fuse and enhance the edge-related voxel features contained in the image stream and border stream and finally achieve the accurate segmentation of pulmonary nodules.

Results

We evaluate the BorDenNet rigorously on the lung public dataset LIDC–IDRI. For the segmentation of the target nodules, the average Dice score is 92.78\(\%\), the average sensitivity is 91.37\(\%\), and the average Hausdorff distance is 3.06 mm. We further test on a private dataset from Shanxi Provincial People’s Hospital, which verifies the excellent generalization of BorDenNet. Our BorDenNet relatively improves the segmentation efficiency for multi-type nodules such as adherent pulmonary nodules and ground-glass pulmonary nodules.

Conclusion

Accurate segmentation of irregular pulmonary nodules can obtain important clinical parameters, which can be used as a guide for clinicians and improve clinical efficiency.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China [61872261,61972274].

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Authors and Affiliations

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030000, Shanxi, China

    Hexi Wang, Ning Xiao, Shichao Luo, Runrui Li, Jun Zhao, Yulan Ma, Juanjuan Zhao & Yan Qiang

  2. College of Information, Jinzhong College of Information, Jinzhong, 030600, Shanxi, China

    Juanjuan Zhao & Long Wang

  3. Cancer Hospital, Shanxi Cancer Hospital, Taiyuan, 030000, Shanxi, China

    Jianhong Lian

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  1. Hexi Wang
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  2. Ning Xiao
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  5. Jun Zhao
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Correspondence to Juanjuan Zhao.

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Wang, H., Xiao, N., Luo, S. et al. Multi-scale dense selective network based on border modeling for lung nodule segmentation. Int J CARS 18, 845–853 (2023). https://doi.org/10.1007/s11548-022-02817-7

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