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. 2020 Sep;15(9):1457-1465.
doi: 10.1007/s11548-020-02228-6. Epub 2020 Jul 16.

Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net

Jiangchang Xu  1 , Shiming Wang  1 , Zijie Zhou  2 , Jiannan Liu  2 , Xiaoyi Jiang  3 , Xiaojun Chen  4
Affiliations

Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net

Jiangchang Xu et al. Int J Comput Assist Radiol Surg. 2020 Sep.

Abstract

Purpose: The analysis of the maxillary sinus (MS) can provide an assessment for many clinical diagnoses, so accurate CT image segmentation of the MS is essential. However, common segmentation methods are mainly done by experienced doctors manually, and there are some challenges such as low efficiency and precision. As for automatic methods, the initial seed points and adjustment of various parameters are required, which will affect the segmentation efficiency. Thus, accurate, efficient, and automatic segmentation method of MS is critical to promote the clinical application.

Methods: This paper proposed an automatic CT image segmentation method of MS based on VGG network and improved V-Net. The VGG network was established to classify CT slices, which can avoid the failure of CT slice segmentation without MS. Then, we proposed the improved V-Net based on edge supervision for segmenting MS regions more effectively. The edge loss was integrated into the loss of the improved V-Net, which could reduce region misjudgment and improve the automatic segmentation performance.

Results: For the classification of CT slices with MS and without MS, the VGG network had a classification accuracy of 97.04 ± 2.03%. In the segmentation, our method obtained a better result, in which the segmentation Dice reached 94.40 ± 2.07%, the Iou (intersection over union) was 90.05 ± 3.26%, and the precision was 94.72 ± 2.64%. Compared with U-Net and V-Net, it reduced region misjudgment significantly and improved the segmentation accuracy. By analyzing the error map of 3D reconstruction, it was mainly distributed in ± 1 mm, which demonstrated that our result was quite close to the ground truth.

Conclusion: The segmentation of the MS can be realized efficiently, accurately, and automatically by our method. Meanwhile, it not only has a better segmentation result, but also improves the doctor's work efficiency, which will have significant impact on clinical applications in the future.

Keywords: Edge supervision; Image segmentation; Improved V-Net; Maxillary sinus.

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