目的:已经开发了许多深度学习方法,用于胸部计算机断层扫描(CT)图像中的肺部病变检测。然而,这些方法通常针对一种特定的病变类型,也就是说,肺结节。在这项工作中,我们打算为更具挑战性的任务开发和评估一种新颖的深度学习方法,检测各种大小差异很大的良性和恶性纵隔病变,形状,强度,以及胸部CT图像中的位置。
方法:我们的纵隔病变检测方法包括两个主要阶段:(a)大小适应性病变候选检测,然后(b)假阳性(FP)减少和良恶性分类。对于候选检测,无锚和一级探测器,即3D-CenterNet旨在定位可疑区域(即,各种大小的候选人)在纵隔内。然后,基于3D-SEResNet的分类器用于区分FP,良性病变,和候选人的恶性病变。
结果:我们通过对相对大规模的数据集进行五次交叉验证来评估所提出的方法,其中包括从甲级三级医院收集的1136名患者的数据。该方法可达到84.3%±1.9%的灵敏度,90.2%±1.4%,93.2%±0.8%,和93.9%±1.1%,分别,在1/8、1/4、½时发现所有良性和恶性病变,每次扫描1个FP,良恶性分类的准确率可达78.7%±2.5%。
结论:所提出的方法可以有效地检测各种大小的纵隔病灶,形状,以及胸部CT图像中的位置。它可以集成到大多数现有的肺部病变检测系统中,以促进其临床应用。该方法还可以容易地扩展到其他类似的3D病变检测任务。
OBJECTIVE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend to develop and evaluate a novel deep learning method for a more challenging task, detecting various benign and malignant mediastinal lesions with wide variations in sizes, shapes, intensities, and locations in chest CT images.
METHODS: Our method for mediastinal lesion detection contains two main stages: (a) size-adaptive lesion candidate detection followed by (b) false-positive (FP) reduction and benign-malignant classification. For candidate detection, an anchor-free and one-stage detector, namely 3D-CenterNet is designed to locate suspicious regions (i.e., candidates with various sizes) within the mediastinum. Then, a 3D-SEResNet-based classifier is used to differentiate FPs, benign lesions, and malignant lesions from the candidates.
RESULTS: We evaluate the proposed method by conducting five-fold cross-validation on a relatively large-scale dataset, which consists of data collected on 1136 patients from a grade A tertiary hospital. The method can achieve sensitivity scores of 84.3% ± 1.9%, 90.2% ± 1.4%, 93.2% ± 0.8%, and 93.9% ± 1.1%, respectively, in finding all benign and malignant lesions at 1/8, 1/4, ½, and 1 FPs per scan, and the accuracy of benign-malignant classification can reach up to 78.7% ± 2.5%.
CONCLUSIONS: The proposed method can effectively detect mediastinal lesions with various sizes, shapes, and locations in chest CT images. It can be integrated into most existing pulmonary lesion detection systems to promote their clinical applications. The method can also be readily extended to other similar 3D lesion detection tasks.