Academy of Scientific and Innovative Research (AcSIR), Chennai, India.
Computational Instrumentation, CSIR-Central Scientific Instruments Organisation, Chandigarh, 160030, India.
Int J Comput Assist Radiol Surg. 2017 Nov;12(11):1877-1893. doi: 10.1007/s11548-017-1650-1. Epub 2017 Jul 28.
The objective of the present study is to put forward a novel automatic segmentation algorithm to segment pharyngeal and sino-nasal airway subregions on 3D CBCT imaging datasets.
A fully automatic segmentation of sino-nasal and pharyngeal airway subregions was implemented in MATLAB programing environment. The novelty of the algorithm is automatic initialization of contours in upper airway subregions. The algorithm is based on boundary definitions of the human anatomy along with shape constraints with an automatic initialization of contours to develop a complete algorithm which has a potential to enhance utility at clinical level. Post-initialization; five segmentation techniques: Chan-Vese level set (CVL), localized Chan-Vese level set (LCVL), Bhattacharya distance level set (BDL), Grow Cut (GC), and Sparse Field method (SFM) were used to test the robustness of automatic initialization.
Precision and F-score were found to be greater than 80% for all the regions with all five segmentation methods. High precision and low recall were observed with BDL and GC techniques indicating an under segmentation. Low precision and high recall values were observed with CVL and SFM methods indicating an over segmentation. A Larger F-score value was observed with SFM method for all the subregions. Minimum F-score value was observed for naso-ethmoidal and sphenoidal air sinus region, whereas a maximum F-score was observed in maxillary air sinuses region. The contour initialization was more accurate for maxillary air sinuses region in comparison with sphenoidal and naso-ethmoid regions.
The overall F-score was found to be greater than 80% for all the airway subregions using five segmentation techniques, indicating accurate contour initialization. Robustness of the algorithm needs to be further tested on severely deformed cases and on cases with different races and ethnicity for it to have global acceptance in Katradental radKatraiology workflow.
本研究旨在提出一种新的自动分割算法，以分割 3D CBCT 成像数据集上的咽和鼻气道亚区。
在 MATLAB 编程环境中实现了鼻和咽气道亚区的全自动分割。该算法的新颖之处在于上气道亚区轮廓的自动初始化。该算法基于人体解剖边界定义和形状约束，通过自动初始化轮廓来开发一个完整的算法，该算法具有在临床水平上提高实用性的潜力。初始化后，使用五种分割技术：Chan-Vese 水平集 (CVL)、局部 Chan-Vese 水平集 (LCVL)、Bhattacharya 距离水平集 (BDL)、Grow Cut (GC) 和稀疏场方法 (SFM) 来测试自动初始化的鲁棒性。
所有区域的五种分割方法的精度和 F 分数均大于 80%。BDL 和 GC 技术观察到高精度和低召回率，表明存在欠分割。CVL 和 SFM 方法观察到低精度和高召回率，表明存在过分割。所有亚区的 SFM 方法的 F 分数较大。鼻筛窦和蝶窦区的最小 F 分数，上颌窦区的最大 F 分数。与蝶窦和筛窦区域相比，上颌窦区域的轮廓初始化更加准确。
使用五种分割技术，所有气道亚区的总体 F 分数均大于 80%，表明轮廓初始化准确。该算法的鲁棒性需要在严重变形的病例和不同种族和民族的病例中进一步测试，以便在全球范围内接受 Katradental radKatraiology 工作流程。
