Automatic segmentation of orbital wall from CT images via a thin wall region supervision-based multi-scale feature search network

Jiangchang Xu; Dingzhong Zhang; Chunliang Wang; Huifang Zhou; Yinwei Li; Xiaojun Chen

doi:10.1007/s11548-023-02924-z

Automatic segmentation of orbital wall from CT images via a thin wall region supervision-based multi-scale feature search network

Int J Comput Assist Radiol Surg. 2023 Nov;18(11):2051-2062. doi: 10.1007/s11548-023-02924-z. Epub 2023 May 23.

Authors

Jiangchang Xu¹, Dingzhong Zhang¹, Chunliang Wang², Huifang Zhou^{3

4}, Yinwei Li^{5

6}, Xiaojun Chen^{7

8}

Affiliations

¹ Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, Room 925, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Minhang District, Shanghai, 200240, China.
² Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
³ Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁴ Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China.
⁵ Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. dr_yinwei_li@foxmail.com.
⁶ Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China. dr_yinwei_li@foxmail.com.
⁷ Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, Room 925, School of Mechanical Engineering, Shanghai Jiao Tong University, Dongchuan Road 800, Minhang District, Shanghai, 200240, China. xiaojunchen@sjtu.edu.cn.
⁸ Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, China. xiaojunchen@sjtu.edu.cn.

PMID: 37219805
DOI: 10.1007/s11548-023-02924-z

Abstract

Purpose: Orbital wall segmentation is critical for orbital measurement and reconstruction. However, the orbital floor and medial wall are made up of thin walls (TW) with low gradient values, making it difficult to segment the blurred areas of the CT images. Clinically, doctors have to manually repair the missing parts of TW, which is time-consuming and laborious.

Methods: To address these issues, this paper proposes an automatic orbital wall segmentation method based on TW region supervision using a multi-scale feature search network. First of all, in the encoding branch, the densely connected atrous spatial pyramid pooling based on the residual connection is adopted to achieve a multi-scale feature search. Then, for feature enhancement, multi-scale up-sampling and residual connection are applied to perform skip connection of features in multi-scale convolution. Finally, we explore a strategy for improving the loss function based on the TW region supervision, which effectively increases the TW region segmentation accuracy.

Results: The test results show that the proposed network performs well in terms of automatic segmentation. For the whole orbital wall region, the Dice coefficient (Dice) of segmentation accuracy reaches 96.086 ± 1.049%, the Intersection over Union (IOU) reaches 92.486 ± 1.924%, and the 95% Hausdorff distance (HD) reaches 0.509 ± 0.166 mm. For the TW region, the Dice reaches 91.470 ± 1.739%, the IOU reaches 84.327 ± 2.938%, and the 95% HD reaches 0.481 ± 0.082 mm. Compared with other segmentation networks, the proposed network improves the segmentation accuracy while filling the missing parts in the TW region.

Conclusion: In the proposed network, the average segmentation time of each orbital wall is only 4.05 s, obviously improving the segmentation efficiency of doctors. In the future, it may have a practical significance in clinical applications such as preoperative planning for orbital reconstruction, orbital modeling, orbital implant design, and so on.

Keywords: Automatic segmentation; Deep learning; Orbital reconstruction; Orbital wall segmentation.

Abstract

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