Limitations of an automated embolism segmentation method in clinical practice

Article type: Research Article

Authors: Wang, Ruifeng^{a; b} | Yu, Nan^{a; b} | Zhou, Sheng^{a; c} | Dong, Fuwen^{a; c} | Wang, Jun^a | Yin, Nan^a | Bai, Lu^a | Shen, Cong^{a; *} | Guo, Youmin^{a; *}

Affiliations: [a] Department of Radiology, The First Affiliated Hospital of Medical School of Xi’an Jiaotong University, Xi’an, Shaanxi, China | [b] Department of Radiology, The Affiliated Hospital of Shaanxi University of Traditional Chinese Medicine, Shaanxi, China | [c] Department of Radiology, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, Gansu, China

Correspondence: [*] Corresponding authors: Cong Shen and Youmin Guo, Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, 277 Yanta West Road, Xi’an, Shaanxi 710061, China. E-mails: shencong1002@stu.xjtu.edu.cn (C. Shen) and cjr.guoyoumin@vip.163.com (Y. Guo).

Abstract: PURPOSE:Automated pulmonary embolism (PE) segmentation is frequently used as a preprocessing step in the quantitative analysis of pulmonary embolism. Objective of this study is to analyze the potential limitation in automated PE segmentation using clinical cases. METHODS:A database of 304 computer tomography pulmonary angiography (CTPA) examinations was collected and confirmed to be PE. After processing using an automated scheme, two radiologists classified these cases into four groups of A, B, C and D, which represent 4 different segmentation results namely, (1) entire pulmonary artery identified without motivation artifacts, (2) entire pulmonary artery identified with motivation artifacts, (3) part of the pulmonary artery identified, and (4) none of the pulmonary artery identified. Then, the possible failed reasons in PE segmentation were analyzed and determined based on the image characterization of the diseases and the applied CTPA scanning protocols. RESULTS:In the study, 143 (47.0%., 30 (9.9%., 110 (36.2%. and 21 (6.9%. examinations were classified into groups A, B, C and D, respectively. Group C and D included the cases with failed segmentation. Fifteen failure reasons, including intrapulmonary abnormalities, extra-pulmonary abnormalities, diffuse pulmonary diseases, enlarged heart, absolute occluded vessels, embolism attached to artery wall, delayed scan time, skewed location, low scan dose, obvious artifact of superior vena cava, previous chest surgery, congenital deformities of the chest, incorrect positioning, missed images and other unknown reasons, were determined with corresponding case percentages ranging from 0.3%.o 9.2%. CONCLUSIONS:Automated segmentation failures were caused by specific lung diseases, anatomy varieties, improper scan time, improper scan dose, manual errors or other unknown reasons. Realization of those limitations is crucial for developing robust automated schemes to handle these issues in a single pass when a large number of CTPA examinations need to be analyzed.

Keywords: Automated pulmonary embolism (PE) segmentation, Computer tomography pulmonary angiography (CTPA)

DOI: 10.3233/XST-18369

Journal: Journal of X-Ray Science and Technology, vol. 26, no. 4, pp. 667-680, 2018