Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model.
Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model.
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DOI：
10.1038/s41598-018-28100-x
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发表时间：
2018-06-28
影响因子：
4.6
通讯作者：
Vaquero JJ
中科院分区：
文献类型：
--
作者：
Gordaliza PM;Muñoz-Barrutia A;Abella M;Desco M;Sharpe S;Vaquero JJ
结核病（TB）是由结核分枝杆菌引起的一种传染病，可造成肺损伤。影像学检查是评价结核病纵向病程的首选技术。计算机辅助的生物标志物识别通过提供疾病的定量评估简化了放射科医生的工作。肺分割是生物标志物提取之前的步骤。在这项研究中，我们提出了一个自动化的程序，使强大的分割受损的肺有病变附着到实质，并受到呼吸运动伪影的结核分枝杆菌感染模型。其主要步骤是提取健康肺组织和气道树，然后消除模糊边界。它的性能进行了比较，相对于分割使用：（1）半自动工具和（2）基于模糊连通性的方法。由三位专家的注释的多数投票产生的共识分割被认为是我们的基础事实。该方法在大多数最难分割的切片中提高了重叠指标（Dice相似系数，94% ± 4%）和表面相似系数（Hausdorff距离，8.64 mm ± 7.36 mm）。结果表明，产生的精细肺分割可以促进提取有意义的定量数据的疾病负担。
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis that produces pulmonary damage. Radiological imaging is the preferred technique for the assessment of TB longitudinal course. Computer-assisted identification of biomarkers eases the work of the radiologist by providing a quantitative assessment of disease. Lung segmentation is the step before biomarker extraction. In this study, we present an automatic procedure that enables robust segmentation of damaged lungs that have lesions attached to the parenchyma and are affected by respiratory movement artifacts in a Mycobacterium Tuberculosis infection model. Its main steps are the extraction of the healthy lung tissue and the airway tree followed by elimination of the fuzzy boundaries. Its performance was compared with respect to a segmentation obtained using: (1) a semi-automatic tool and (2) an approach based on fuzzy connectedness. A consensus segmentation resulting from the majority voting of three experts’ annotations was considered our ground truth. The proposed approach improves the overlap indicators (Dice similarity coefficient, 94% ± 4%) and the surface similarity coefficients (Hausdorff distance, 8.64 mm ± 7.36 mm) in the majority of the most difficult-to-segment slices. Results indicate that the refined lung segmentations generated could facilitate the extraction of meaningful quantitative data on disease burden.
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