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基于轮廓追踪与校正的胸部CT中含胸膜下结节的肺分割自动方法

Automatic Approach for Lung Segmentation with Juxta-Pleural Nodules from Thoracic CT Based on Contour Tracing and Correction.

作者信息

Wang Jinke, Guo Haoyan

机构信息

Department of Software Engineering, Harbin University of Science and Technology, Rongcheng, China.

School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China.

出版信息

Comput Math Methods Med. 2016;2016:2962047. doi: 10.1155/2016/2962047. Epub 2016 Nov 16.

DOI:10.1155/2016/2962047
PMID:27974907
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5128731/
Abstract

This paper presents a fully automatic framework for lung segmentation, in which juxta-pleural nodule problem is brought into strong focus. The proposed scheme consists of three phases: skin boundary detection, rough segmentation of lung contour, and pulmonary parenchyma refinement. Firstly, chest skin boundary is extracted through image aligning, morphology operation, and connective region analysis. Secondly, diagonal-based border tracing is implemented for lung contour segmentation, with maximum cost path algorithm used for separating the left and right lungs. Finally, by arc-based border smoothing and concave-based border correction, the refined pulmonary parenchyma is obtained. The proposed scheme is evaluated on 45 volumes of chest scans, with volume difference (VD) 11.15 ± 69.63 cm, volume overlap error (VOE) 3.5057 ± 1.3719%, average surface distance (ASD) 0.7917 ± 0.2741 mm, root mean square distance (RMSD) 1.6957 ± 0.6568 mm, maximum symmetric absolute surface distance (MSD) 21.3430 ± 8.1743 mm, and average time-cost 2 seconds per image. The preliminary results on accuracy and complexity prove that our scheme is a promising tool for lung segmentation with juxta-pleural nodules.

摘要

本文提出了一种用于肺部分割的全自动框架，其中胸膜旁结节问题得到了重点关注。所提出的方案包括三个阶段：皮肤边界检测、肺轮廓的粗略分割以及肺实质细化。首先，通过图像对齐、形态学操作和连通区域分析来提取胸部皮肤边界。其次，采用基于对角线的边界追踪进行肺轮廓分割，并使用最大代价路径算法来分离左右肺。最后，通过基于弧线的边界平滑和基于凹面的边界校正，获得细化的肺实质。该方案在45例胸部扫描图像上进行了评估，体积差异（VD）为11.15±69.63cm，体积重叠误差（VOE）为3.5057±1.3719%，平均表面距离（ASD）为0.7917±0.2741mm，均方根距离（RMSD）为1.6957±0.6568mm，最大对称绝对表面距离（MSD）为21.3430±8.1743mm，平均每张图像的时间成本为2秒。在准确性和复杂性方面的初步结果证明，我们的方案是一种用于分割伴有胸膜旁结节的肺部的有前景的工具。

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本文引用的文献

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2
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Med Image Comput Comput Assist Interv. 2011;14(Pt 3):667-74. doi: 10.1007/978-3-642-23626-6_82.
3
Automated 3-D segmentation of lungs with lung cancer in CT data using a novel robust active shape model approach.
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Front Oncol. 2023 Jul 20;13:1196414. doi: 10.3389/fonc.2023.1196414. eCollection 2023.
4
A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.基于 CT 图像的肺部及其叶部自动分割方法和公共数据集的系统评价及相关发现。
Yearb Med Inform. 2022 Aug;31(1):277-295. doi: 10.1055/s-0042-1742517. Epub 2022 Dec 4.
5
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IEEE J Transl Eng Health Med. 2018 May 18;6:1800513. doi: 10.1109/JTEHM.2018.2837901. eCollection 2018.
利用一种新颖的稳健主动形状模型方法对 CT 数据中的肺癌进行自动三维肺分割。
IEEE Trans Med Imaging. 2012 Feb;31(2):449-60. doi: 10.1109/TMI.2011.2171357. Epub 2011 Oct 13.
4
Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection.使用带有错误检测的混合方法从胸部计算机断层扫描中自动进行肺部分割。
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5
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6
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7
Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images.胸部CT图像中用于纳入胸膜结节和肺血管的分割肺边界校正
Comput Biol Med. 2008 Aug;38(8):845-57. doi: 10.1016/j.compbiomed.2008.04.012. Epub 2008 Jun 26.
8
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Comput Med Imaging Graph. 2008 Sep;32(6):452-62. doi: 10.1016/j.compmedimag.2008.04.005. Epub 2008 Jun 2.
9
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10
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