Cheng Yu-Kai, Lin Chih-Lung, Huang Yi-Chi, Chen Jui-Chi, Lan Tzu-Peng, Lian Zhen-You, Chuang Cheng-Hung
Department of Neurosurgery, China Medical University Hospital, Taichung 404, Taiwan.
Department of Neurosurgery, Asia University Hospital, Taichung 413, Taiwan.
J Clin Med. 2021 Oct 17;10(20):4760. doi: 10.3390/jcm10204760.
The automatic segmentation of intervertebral discs from medical images is an important task for an intelligent clinical system. In this study, a deep learning model based on the MultiResUNet model for the automatic segmentation of specific intervertebral discs is presented. MultiResUNet can easily segment all intervertebral discs in MRI images; however, when only certain specific intervertebral discs need to be segmented, problems with segmentation errors, misalignment, and noise occur. In order to solve these problems, a two-stage MultiResUNet model is proposed. Connected-component labeling, automatic cropping, and distance transform are used in the proposed method. The experimental results show that the segmentation errors and misalignments of specific intervertebral discs are greatly reduced, and the segmentation accuracy is increased to about 94%. The performance of the proposed method proves its usefulness for the automatic segmentation of specific intervertebral discs over other deep learning models, such as the U-Net, CNN-based, Attention U-Net, and MultiResUNet models.
从医学图像中自动分割椎间盘是智能临床系统的一项重要任务。在本研究中，提出了一种基于MultiResUNet模型的深度学习模型，用于特定椎间盘的自动分割。MultiResUNet可以轻松分割MRI图像中的所有椎间盘；然而，当只需要分割某些特定的椎间盘时，就会出现分割错误、错位和噪声等问题。为了解决这些问题，提出了一种两阶段的MultiResUNet模型。所提方法使用了连通分量标记、自动裁剪和距离变换。实验结果表明，特定椎间盘的分割错误和错位大大减少，分割准确率提高到了约94%。所提方法的性能证明了其在特定椎间盘自动分割方面相对于其他深度学习模型（如U-Net、基于卷积神经网络的模型、注意力U-Net和MultiResUNet模型）的有效性。
