利用自适应区域生长和基于形状的形态学对全肺和转移性肺结节进行三维分割。

IF 1 4区 医学 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Esha Baidya Kayal, Shuvadeep Ganguly, Archana Sasi, D S Dheeksha, Manish Saini, Swetambri Sharma, Shivansh Gupta, Nikhil Sharma, Krithika Rangarajan, Sameer Bakhshi, Devasenathipathy Kandasamy, Amit Mehndiratta
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引用次数: 0

摘要

目的:早期诊断原发性和转移性肺结节对制定有效的治疗方案至关重要。手工描绘肺结节不具有时间效率,容易出现人为错误以及观察者之间和观察者内部的差异。本研究旨在解决开源计算机辅助检测(CAD)系统的未满足需求,该系统用于肺和转移性肺结节的三维分割以及放射学特征提取。方法:本文提出的基于自适应区域生长的肺结节分割(RGLNS)工具是自主开发的,只需手动输入即可在计算机断层扫描(CT)图像上选择结节内的种子点。共筛选了100例肉瘤患者的230张CT扫描图。在200个CT扫描中发现肺结节,进一步分析。肺和结节分割的准确性使用5分Likert量表进行定性评估(无法解释:1;差:2;公平:3;好:4;优秀:5)定量运用Dice系数和Jaccard指数。结果:共分析200张CT扫描片,共12000张CT切片,其中发现肺结节786个。定量肺分割准确率(n=2400片)的Dice系数为0.92±0.06,Jaccard指数为0.85±0.05。肺边界矫正(4.56±1.18)和肺血管包含(4.75±0.72)定性评分(n=9600片)为良至优。单发结节(n=73)、胸膜旁结节(n=32)、血管旁结节(n=28)、裂隙附着结节(n=6)、毛玻璃结节(n=6)的定量结节分割准确率分别为dice系数0.92±0.03、0.88±0.04、0.86±0.03、0.85±0.03、084±0.04、Jaccard指数0.78±0.02、0.76±0.04。结节边界定性评分(n=644个)为良至优[孤立性(n=342): 4.97±0.15;胸膜旁(n=155): 4.45±0.60;血管旁(n=127): 4.40±0.65;裂隙附着(n=9): 4.40±0.70;毛玻璃(n=11): 4.25±0.75)和肺血管/胸膜对结节的排除效果良好[胸膜旁(n=155): 4.10±0.66;血管旁(n=127): 4.08±0.64;裂隙附着(n=9): 4.30±0.67]。结论:提出的半自动化CAD系统RGLNS,需要最少的人工输入,对整个肺和各种类型的转移性肺结节的分割结果显示出鲁棒性和前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3D Segmentation of Whole Lung and Metastatic Lung Nodules Using Adaptive Region Growing and Shape-based Morphology.

Objective: Early diagnosis of primary and metastatic lung nodules is critical for effective therapeutic planning. Manual delineation of lung nodules is not time-efficient and is prone to human error as well as interobserver and intraobserver variability. This study aimed to address the unmet need for an open-source computer-aided detection (CAD) system for 3D segmentation of lung and metastatic lung nodules along with radiomic feature extraction.

Methods: The proposed adaptive region-growing-based lung nodule segmentation (RGLNS) tool was developed in-house, requiring only manual input to select a seed point within the nodule on computed tomography (CT) images. A total of 230 CT scans from 100 patients with sarcomas were screened. Lung nodules were present in 200 CT scans, which were further analyzed. The accuracy of the lung and nodule segmentation was evaluated qualitatively using a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) and quantitatively using the Dice coefficient and Jaccard index.

Results: A total of 200 CT scans comprising 12,000 CT slices were analyzed, among which 786 lung nodules were identified. Quantitative lung segmentation accuracies (n=2400 slices) yielded a Dice coefficient of 0.92±0.06 and a Jaccard index of 0.85±0.05. Qualitative scores (n=9600 slices) for lung boundary correction (4.56±1.18) and inclusion of pulmonary vessels (4.75±0.72) were rated as good to excellent. Quantitative nodule segmentation (n=142 nodules) accuracies were as follows: dice coefficient=0.92±0.03, 0.88±0.04, 0.86±0.03, 0.85±0.03, 084±0.04 and Jaccard index=0.84±0.03, 0.81±0.04, 0.78±0.04, 0.78±0.02, 0.76±0.04 for solitary (n=73), juxtapleural (n=32), juxtavascular (n=28), fissure-attached (n=6), and ground-glass (n=6) nodules, respectively. Qualitative scores (n=644 nodules) for nodule-boundary were good to excellent [solitary (n=342): 4.97±0.15; juxtapleural (n=155): 4.45±0.60; juxtavascular (n=127): 4.40±0.65; fissure-attached (n=9): 4.40±0.70; ground-glass (n=11): 4.25±0.75] and for exclusion of pulmonary vessels/pleura from nodules were good [juxtapleural (n=155): 4.10±0.66; juxtavascular (n=127): 4.08±0.64; fissure-attached (n=9): 4.30±0.67].

Conclusions: The proposed semiautomated CAD system, RGLNS, requiring minimal manual input, demonstrated robust, and promising segmentation results for the whole lung and various types of metastatic lung nodules.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).
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