Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection
Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection
复制标题
用于肺结节检测中减少假阳性的多级上下文3 - D卷积神经网络
DOI：
10.1109/tbme.2016.2613502
复制
发表时间：
2017-07-01
影响因子：
4.6
通讯作者：
Heng, Pheng-Ann
中科院分区：
文献类型：
--
作者：
Dou, Qi;Chen, Hao;Heng, Pheng-Ann
目的：假阳性减少是肺结节自动检测系统中最关键的组成部分之一，在肺癌的诊断和早期治疗中起着重要作用。本文的目的是有效地解决这一任务的挑战，从而准确地区分真正的结核从大量的候选人。研究方法：我们提出了一种新的方法，采用三维（3-D）卷积神经网络（CNN）的假阳性减少自动肺结节检测体积计算机断层扫描（CT）扫描。与2-D CNN相比，3-D CNN可以编码更丰富的空间信息，并通过用3-D样本训练的层次结构来提取更具代表性的特征。更重要的是，我们进一步提出了一个简单而有效的策略来编码多层次的上下文信息，以满足肺结节的大变化和硬模仿带来的挑战。结果如下：拟议的框架已在与ISBI 2016联合举办的LUNA 16挑战赛中得到广泛验证，我们在误报减少轨道中获得了最高的竞争性能指标（CPM）分数。结论：实验结果证明了将多级上下文信息集成到3-D CNN框架中用于体积CT数据中的自动肺结节检测的重要性和有效性。重要性：虽然我们的方法是专为肺结节检测，所提出的框架是通用的，可以很容易地扩展到许多其他3-D对象检测任务的体积医学图像，其中的目标对象有很大的变化，并伴随着一些硬模仿。
Objective: False positive reduction is one of the most crucial components in an automated pulmonary nodule detection system, which plays an important role in lung cancer diagnosis and early treatment. The objective of this paper is to effectively address the challenges in this task and therefore to accurately discriminate the true nodules from a large number of candidates. Methods: We propose a novel method employing three-dimensional (3-D) convolutional neural networks (CNNs) for false positive reduction in automated pulmonary nodule detection from volumetric computed tomography (CT) scans. Compared with its 2-D counterparts, the 3-D CNNs can encode richer spatial information and extract more representative features via their hierarchical architecture trained with 3-D samples. More importantly, we further propose a simple yet effective strategy to encode multilevel contextual information to meet the challenges coming with the large variations and hard mimics of pulmonary nodules. Results: The proposed framework has been extensively validated in the LUNA16 challenge held in conjunction with ISBI 2016, where we achieved the highest competition performance metric (CPM) score in the false positive reduction track. Conclusion: Experimental results demonstrated the importance and effectiveness of integrating multilevel contextual information into 3-D CNN framework for automated pulmonary nodule detection in volumetric CT data. Significance: While our method is tailored for pulmonary nodule detection, the proposed framework is general and can be easily extended to many other 3-D object detection tasks from volumetric medical images, where the targeting objects have large variations and are accompanied by a number of hard mimics.