Azam Sami, Montaha Sidratul, Rafid A K M Rakibul Haque, Karim Asif, Jonkman Mirjam, De Boer Friso, McCallum Gabrielle, Masters Ian Brent, Chang Anne
Faculty of Science and Technology, Charles Darwin University, Casuarina, NT 0909, Australia.
Child Health Division, Menzies School of Health Research, Darwin, NT 0811, Australia.
Biomedicines. 2023 Jun 30;11(7):1874. doi: 10.3390/biomedicines11071874.
Bronchiectasis in children can progress to a severe lung condition if not diagnosed and treated early. The radiological diagnostic criteria for the diagnosis of bronchiectasis is an increased broncho-arterial (BA) ratio. From high-resolution computed tomography (HRCT) scans, the BA pairs must be detected first to derive the BA ratio. This study aims to identify potential BA pairs from HRCT scans of children undertaken to evaluate suppurative lung disease through an automated approach. After segmenting the lung regions, the HRCT scans are cleaned using a histogram analysis-based approach followed by a potential arteries identification process comprising four conditions based on imaging features. Potential arteries and their connected components are extracted, and potential bronchi are identified. Finally, the coordinates of potential arteries and potential bronchi are matched as the last step of BA pairs extraction. A total of 8-50 BA pairs are detected for each patient. Additionally, the area and several diameters of the bronchi and arteries are measured, and BA ratios based on these are calculated. Through this approach, the BA pairs of a CT scan datasets are detected and utilizing a deep learning model, a high classification test accuracy of 98.53% is achieved, validating the robustness of the proposed BA detection approach. The results show that visible BA pairs can be identified and segmented automatically, and the BA ratio calculated may help diagnose bronchiectasis with less effort and time.
儿童支气管扩张若不及早诊断和治疗,可能会发展为严重的肺部疾病。支气管扩张的放射学诊断标准是支气管动脉(BA)比值增加。从高分辨率计算机断层扫描(HRCT)图像中,必须先检测出BA对,才能得出BA比值。本研究旨在通过自动化方法,从用于评估化脓性肺部疾病的儿童HRCT扫描图像中识别潜在的BA对。在对肺区域进行分割后,使用基于直方图分析的方法对HRCT扫描图像进行清理,随后进行基于成像特征的包含四个条件的潜在动脉识别过程。提取潜在动脉及其连通组件,并识别潜在支气管。最后,作为BA对提取的最后一步,匹配潜在动脉和潜在支气管的坐标。每位患者共检测到8至50对BA。此外,测量支气管和动脉的面积及多个直径,并据此计算BA比值。通过这种方法,检测出CT扫描数据集的BA对,并利用深度学习模型实现了98.53%的高分类测试准确率,验证了所提出的BA检测方法的稳健性。结果表明,可以自动识别和分割可见的BA对,计算出的BA比值可能有助于更轻松、更快速地诊断支气管扩张。