目的:肺腔病变是由多种恶性和非恶性疾病引起的肺部常见病变之一。腔病变的诊断通常基于对典型形态特征的准确识别。基于深度学习的模型来自动检测,段,并量化CT扫描上的空腔病变区域在临床诊断中具有潜力,监测,和治疗效果评估。
方法:本文提出了一种名为CSA2-ResNet的基于弱监督深度学习的方法来定量表征空腔病变。首先使用预训练的2D分割模型对肺实质进行分割,然后将有或没有空腔损伤的输出输入包含混合注意力模块的开发的深度神经网络。接下来,可视化病变是使用梯度加权类激活映射从分类网络的激活区域生成的,并应用图像处理进行后处理以获得预期的空腔病变分割结果。最后,空腔病变的自动特征测量(例如,面积和厚度)进行了开发和验证。
结果:提出的弱监督分割方法获得了准确性,精度,特异性,召回,F1得分为98.48%,96.80%,97.20%,100%,98.36%,分别。与其他方法相比有显著的改善(P<0.05)。形貌的定量表征也获得了良好的分析效果。
结论:提出的易于训练和高性能的深度学习模型为临床上肺腔病变的诊断和动态监测提供了一种快速有效的方法。临床和转化影响声明:该模型使用人工智能来实现CT扫描中肺腔病变的检测和定量分析。实验中揭示的形态学特征可以作为诊断和动态监测空腔病变患者的潜在指标。
OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteristics. A deep learning-based model to automatically detect, segment, and quantify the region of cavity lesion on CT scans has potential in clinical diagnosis, monitoring, and treatment efficacy assessment.
METHODS: A weakly-supervised deep learning-based method named CSA2-ResNet was proposed to quantitatively characterize cavity lesions in this paper. The lung parenchyma was firstly segmented using a pretrained 2D segmentation model, and then the output with or without cavity lesions was fed into the developed deep neural network containing hybrid attention modules. Next, the visualized lesion was generated from the activation region of the classification network using gradient-weighted class activation mapping, and image processing was applied for post-processing to obtain the expected segmentation results of cavity lesions. Finally, the automatic characteristic measurement of cavity lesions (e.g., area and thickness) was developed and verified.
RESULTS: the proposed weakly-supervised segmentation method achieved an accuracy, precision, specificity, recall, and F1-score of 98.48%, 96.80%, 97.20%, 100%, and 98.36%, respectively. There is a significant improvement (P < 0.05) compared to other methods. Quantitative characterization of morphology also obtained good analysis effects.
CONCLUSIONS: The proposed easily-trained and high-performance deep learning model provides a fast and effective way for the diagnosis and dynamic monitoring of pulmonary cavity lesions in clinic. Clinical and Translational Impact Statement: This model used artificial intelligence to achieve the detection and quantitative analysis of pulmonary cavity lesions in CT scans. The morphological features revealed in experiments can be utilized as potential indicators for diagnosis and dynamic monitoring of patients with cavity lesions.