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.

中文翻译：

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基于VGG网络和改进V-Net的上颌窦CT图像自动分割[J].

目的

上颌窦 (MS) 的分析可以为许多临床诊断提供评估，因此对 MS 进行准确的 CT 图像分割至关重要。然而，常见的分割方法主要由有经验的医生手动完成，存在效率低、精度低等挑战。对于自动方法，需要初始种子点和各种参数的调整，这会影响分割效率。因此，准确、高效、自动的 MS 分割方法对于促进临床应用至关重要。

方法

本文提出了一种基于VGG网络和改进的V-Net的MS自动CT图像分割方法。建立VGG网络对CT切片进行分类，可以避免没有MS的CT切片分割失败。然后，我们提出了基于边缘监督的改进 V-Net，以更有效地分割 MS 区域。边缘损失被整合到改进的V-Net的损失中，可以减少区域误判，提高自动分割性能。

结果

对于带 MS 和不带 MS 的 CT 切片分类，VGG 网络的分类精度为 97.04 ± 2.03%。在分割中，我们的方法获得了较好的结果，分割Dice达到了94.40±2.07%，Iou(intersection over union)为90.05±3.26%，精度为94.72±2.64%。与U-Net和V-Net相比，显着减少了区域误判，提高了分割精度。通过分析3D重建的误差图，主要分布在±1mm范围内，这表明我们的结果非常接近ground truth。

结论

通过我们的方法可以高效、准确和自动地实现 MS 的分割。同时，它不仅具有更好的分割结果，而且提高了医生的工作效率，对未来的临床应用将产生重大影响。