Discontinuity in the delineation of peripheral bronchioles hinders the potential clinical application of automated airway segmentation models. Moreover, the deployment of such models is limited by the data heterogeneity across different centres, and pathological abnormalities also make achieving accurate robust segmentation in distal small airways difficult. Meanwhile, the diagnosis and prognosis of lung diseases often rely on evaluating structural changes in those anatomical regions. To address this gap, this paper presents a patch-scale adversarial-based refinement network that takes in preliminary segmentation along with original CT images and outputs a refined mask of the airway structure. The method is validated on three different datasets encompassing healthy cases, cases with cystic fibrosis and cases with COVID-19. The results are quantitatively evaluated by seven metrics and achieved more than a 15% rise in detected length ratio and detected branch ratio, showing promising performance compared to previously proposed models. The visual illustration also proves our refinement guided by a patch-scale discriminator and centreline objective functions is effective in detecting discontinuities and missing bronchioles. Furthermore, the generalizability of our refinement pipeline is tested on three previous models and improves their segmentation completeness significantly.
翻译:在划定外围支气管时的不连续性妨碍了自动空气路分割模型的潜在临床应用。此外,由于不同中心的数据不均,这些模型的部署受到限制,不同中心的数据异质性限制了这些模型的部署,病态异常也使得在阴性小空气道中很难实现准确稳健的分解;同时,肺病的诊断和预测往往依赖评价这些解剖区域的结构性变化;为弥补这一差距,本文件展示了一个跨规模的对抗性改进网络,在初步分解和原始CT图像和产出的同时,对空气路结构进行精细化遮罩。该方法在三个不同的数据集上得到验证,其中包括健康病例、细胞纤维化病例和COVID-19病例。其结果通过7度的定量评估,在检测到的长度比率和检测到的分支比率上提高了15%以上,显示出与先前提出的模型相比有希望的绩效。视觉说明还证明了我们借助一个偏差规模的区分和中心线客观功能来进行精细化。此外,在检测不连续和缺失的支架状体结构方面,对三条形模型进行了大幅度的完善性测试。此外,还检验了我们管道的精准性。