Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT
Thoracic cavity segmentation algorithm using multiorgan extraction and surface fitting in volumetric CT
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DOI：
10.1118/1.4866836
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发表时间：
2014-04-01
期刊：
影响因子：
3.8
通讯作者：
Kim, Hee Chan
中科院分区：
文献类型：
--
作者：
Bae, JangPyo;Kim, Namkug;Kim, Hee Chan
目的：目的：建立和验证一种用于慢性阻塞性肺疾病（COPD）患者胸腔容积和纵隔脂肪定量的半自动分割方法。方法：通过分割肋骨、肺、心脏和膈肌等多器官来分割胸腔区域。为了涵盖各种肺部疾病引起的变化，通过使用三维表面拟合方法对胸廓内壁和横膈膜进行建模。为了提高膈肌表面模型的准确性，采用基于形状先验的两阶段水平集方法对心脏及其周围组织进行分割。为了评估所提出的算法的准确性，将50名患者的算法结果与两名具有5年以上经验的专家的手动分割结果进行了比较（这些手动结果由专家胸放射科医生确认）。所提出的方法进行了比较，三个国家的最先进的分割方法。用于评价分割准确性的指标是体积重叠率（VOR）、VOR上的假阳性率（FPRV）、VOR上的假阴性率（FNRV）、平均对称绝对表面距离（ASASD）、平均对称平方表面距离（ASSSD）和最大对称表面距离（MSSD）。在胸腔容量测定方面，所提出的方法的平均+/- SD VOR、FPRV和FNRV为分别为（98.17 ± 0.84）%、（0.49 ± 0.23）%和（1.34 ± 0.83）%。胸壁的ASASD、ASSSD和MSSD分别为0.28 ± 0.12、1.28 ± 0.53和23.91 ± 7.64 mm。隔膜表面的ASASD、ASSSD和MSSD分别为1.73 +/- 0.91、3.92 +/- 1.68和27.80 +/- 10.63 mm。所提出的方法进行显着优于其他三种方法VOR，ASASD，和ASSSD.Conclusions：所提出的半自动胸腔分割方法，提取多个器官（即肋骨，胸壁，膈肌，心脏），具有很高的准确性，并可能是有用的临床目的。(C)2014年作者。所有文章内容，除非另有说明，是根据知识共享署名3.0未移植许可证许可。
Purpose: To develop and validate a semiautomatic segmentation method for thoracic cavity volumetry and mediastinum fat quantification of patients with chronic obstructive pulmonary disease.Methods: The thoracic cavity region was separated by segmenting multiorgans, namely, the rib, lung, heart, and diaphragm. To encompass various lung disease-induced variations, the inner thoracic wall and diaphragm were modeled by using a three-dimensional surface-fitting method. To improve the accuracy of the diaphragm surface model, the heart and its surrounding tissue were segmented by a two-stage level set method using a shape prior. To assess the accuracy of the proposed algorithm, the algorithm results of 50 patients were compared to the manual segmentation results of two experts with more than 5 years of experience (these manual results were confirmed by an expert thoracic radiologist). The proposed method was also compared to three state-of-the-art segmentation methods. The metrics used to evaluate segmentation accuracy were volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), false negative ratio on VOR (FNRV), average symmetric absolute surface distance (ASASD), average symmetric squared surface distance (ASSSD), and maximum symmetric surface distance (MSSD).Results: In terms of thoracic cavity volumetry, the mean +/- SD VOR, FPRV, and FNRV of the proposed method were (98.17 +/- 0.84)%, (0.49 +/- 0.23)%, and (1.34 +/- 0.83)%, respectively. The ASASD, ASSSD, and MSSD for the thoracic wall were 0.28 +/- 0.12, 1.28 +/- 0.53, and 23.91 +/- 7.64 mm, respectively. The ASASD, ASSSD, and MSSD for the diaphragm surface were 1.73 +/- 0.91, 3.92 +/- 1.68, and 27.80 +/- 10.63 mm, respectively. The proposed method performed significantly better than the other three methods in terms of VOR, ASASD, and ASSSD.Conclusions: The proposed semiautomatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart), performed with high accuracy and may be useful for clinical purposes. (C) 2014 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 Unported License.