Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Oberdürrbacher Str. 6, 97080, Würzburg, Germany.
BMC Med Imaging. 2021 May 8;21(1):79. doi: 10.1186/s12880-021-00608-1.
Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters.
In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen-Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson's correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer.
The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson's correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L.
Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.
功能肺部 MRI 技术通常与耗时的后处理相关联，其中手动肺部分割是最繁琐的部分。本研究的目的是探讨利用堆叠螺旋轨迹扫描的快速 UTE 序列进行肺部图像的深度学习分割是否可以为功能参数的计算提供足够准确的结果。
本研究中，通过快速 UTE 序列（堆叠螺旋轨迹和最短回波时间为 0.05ms）对 20 例囊性纤维化（CF）患者和 33 例健康志愿者进行肺部图像采集。然后，使用 17713 个 2D 冠状切片和手动分割获得的标签，对卷积神经网络进行语义肺部分割训练。随后，将该网络应用于 4920 个独立的 2D 测试图像，并使用索伦森-迪塞相似系数（DSC）和哈氏距离（HD）将结果与手动分割进行比较。使用 Pearson 相关系数和 Bland Altman 分析比较两种分割方法获得的肺容积和分数通气值。为了研究对 CF 患者以外的患者的泛化能力，特别是对肺部存在较大实变的患者，还将该网络应用于 4 例肺炎患者和 1 例肺癌患者的 UTE 图像。
肺组织的总体 DSC 为 0.967±0.076（平均值±标准差），HD 为 4.1±4.4mm。手动和基于深度学习的分割得到的肺容积以及分数通气值均具有很高的总体相关性（Pearson 相关系数为 0.99 和 1.00）。对于具有未知病理学/实变的额外队列，平均 DSC 为 0.930±0.083，HD=12.9±16.2mm，肺容积的平均差值为 0.032±0.048L。
基于堆叠螺旋的肺部 MRI 中基于深度学习的图像分割可以准确估计肺容积和分数通气值，并有望在未来取代耗时的手动图像分割步骤。
