分割
人工智能
计算机科学
模式识别(心理学)
生成对抗网络
模式(遗传算法)
人工神经网络
图像分割
计算机视觉
深度学习
机器学习
作者
James S. Tan,Longlong Jing,Yumei Huo,Lihong Li,Oğuz Akın,Yingli Tian
标识
DOI:10.1016/j.compmedimag.2020.101817
摘要
Lung segmentation in Computerized Tomography (CT) images plays an important role in various lung disease diagnosis. Most of the current lung segmentation approaches are performed through a series of procedures with manually empirical parameter adjustments in each step. Pursuing an automatic segmentation method with fewer steps, we propose a novel deep learning Generative Adversarial Network (GAN)-based lung segmentation schema, which we denote as LGAN. The proposed schema can be generalized to different kinds of neural networks for lung segmentation in CT images. We evaluated the proposed LGAN schema on datasets including Lung Image Database Consortium image collection (LIDC-IDRI) and Quantitative Imaging Network (QIN) collection with two metrics: segmentation quality and shape similarity. Also, we compared our work with current state-of-the-art methods. The experimental results demonstrated that the proposed LGAN schema can be used as a promising tool for automatic lung segmentation due to its simplified procedure as well as its improved performance and efficiency.
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