一项关于CT影像中肺部分割和结节检测的2.5D方法的调研和分类。
A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT images.
发表日期：2023 Sep 04
作者：
R Jenkin Suji, Sarita Singh Bhadauria, W Wilfred Godfrey
来源：
COMPUTERS IN BIOLOGY AND MEDICINE
摘要：
肺癌诊断和检测的计算机辅助诊断系统能够提供无偏差、无疲劳的诊断，方差很小，从而降低死亡率和五年生存率。肺分割和肺结节检测是肺癌计算机辅助诊断系统管道中的关键步骤。关于肺分割和肺结节检测的文献主要包括处理三维数据或二维切片的技术和调查。然而，仍需添加突出2.5D技术的肺分割和肺结节检测调查。本文提供了2.5D方法的背景和讨论以填补这个空白。此外，本文还给出了2.5D方法的分类和详细描述。根据分类，将肺分割和肺结节检测的各种2.5D技术归类为这些2.5D方法，然后提出了在这个方向上可能的未来工作。版权所有 © 2023 Elsevier Ltd. 保留所有权利。
CAD systems for lung cancer diagnosis and detection can significantly offer unbiased, infatiguable diagnostics with minimal variance, decreasing the mortality rate and the five-year survival rate. Lung segmentation and lung nodule detection are critical steps in the lung cancer CAD system pipeline. Literature on lung segmentation and lung nodule detection mostly comprises techniques that process 3-D volumes or 2-D slices and surveys. However, surveys that highlight 2.5D techniques for lung segmentation and lung nodule detection still need to be included. This paper presents a background and discussion on 2.5D methods to fill this gap. Further, this paper also gives a taxonomy of 2.5D approaches and a detailed description of the 2.5D approaches. Based on the taxonomy, various 2.5D techniques for lung segmentation and lung nodule detection are clustered into these 2.5D approaches, which is followed by possible future work in this direction.Copyright © 2023 Elsevier Ltd. All rights reserved.