Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks

Junqian Zhang, Ying-Zhi Sun, Hongen Liao, Jian Zhu, Yuan Zhang
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引用次数: 3

Abstract

Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.
基于深度卷积神经网络的MVCT腮腺自动分割
放射性口干症是头颈部肿瘤放射治疗中的一个主要问题,其主要原因是过量照射对腮腺的损伤。基于螺旋断层扫描的MVCT成像可用于监测腮腺的连续变化。人工分割耗时、费力、主观,而自动分割由于头颈部复杂的解剖环境以及MVCT图像中存在的噪声,具有很大的挑战性。在本文中,我们提出了一种定位-细化方案来分割MVCT中的腮腺。在数据预处理后,我们在数据预处理后的定位阶段使用掩模区域卷积神经网络(mask R-CNN),并在接下来的精细分割阶段设计改进的U-Net。据我们所知,这项研究是深度学习在MVCT分割方面的开创性工作。基于不同的头颈部mvct数据分布和不同的分割模型的综合实验证明了我们的方法在准确性、有效性、灵活性和实用性方面的优势。我们的方法可以作为辐射损伤研究的有力工具,其中准确的器官分割是至关重要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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