Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies.
Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies.
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DOI：
10.3390/jimaging6110125
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发表时间：
2020-11-19
影响因子：
3.2
通讯作者：
Stefano A
中科院分区：
文献类型：
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作者：
Comelli A;Coronnello C;Dahiya N;Benfante V;Palmucci S;Basile A;Vancheri C;Russo G;Yezzi A;Stefano A
背景资料：这项工作的目的是使用特发性肺纤维化患者的高分辨率计算机断层扫描图像的非常小的数据集，确定一种应用于实质的自动，准确和快速的深度学习分割方法。通过这种方式，我们的目标是增强医疗保健操作员在放射组学研究中执行的方法，其中必须使用独立于操作员的分割方法来正确识别目标，从而识别基于纹理的预测模型。研究方法：研究了两种深度学习模型：（i）U-Net，已用于许多生物医学图像分割任务，以及（ii）E-Net，用于自动驾驶汽车中的图像分割任务，其中硬件可用性有限，准确的分割对用户安全至关重要。我们的小型图像数据集由42项特发性肺纤维化患者的研究组成，其中只有32项用于训练阶段。我们比较了这两种模型的性能方面的相似性，其分割结果与黄金标准，并在其资源的要求。结果如下：E-Net可用于获得准确（骰子相似系数= 95.90%）、快速（20.32 s）和临床可接受的肺区域分割。结论：我们证明，深度学习模型可以有效地应用于快速分割和量化肺纤维化患者的实质，而无需任何放射科医生的监督，以产生用户独立的结果。
Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.