论著摘要 |【AI-CT】肺结节检测假阳性减少的多层次背景三维CNNs(双语版)

2018-01-26 11:47:43 admin 0
标签:   人工智能 肺结节 CT CNN 3-D CNN 卷积神经网络

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection.

发表日期: 2016.09.26   来源:IEEE Trans Biomed Eng. 2017 Jul;64(7):1558-1567.

作者:

Dou Q1, Chen H1, Yu L1, Qin J2, Heng PA1.

作者介绍:

1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong.

2. Centre for Smart Health, School of NursingThe Hong Kong Polytechnic University

摘要

Abstact

目标

假阳性减少是肺结节自动检测系统中最重要的组成部分之一,在肺癌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中的挑战,从而准确地将真正的结核与众多候选人区分开来。

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.

方法

我们提出了一种采用三维(3-D)卷积神经网络(CNN)进行体积计算机断层扫描(CT)自动肺结节检测的假阳性减少的新方法。与其二维相比,三维CNN可以编码更丰富的空间信息,并通过三维样本训练的分层结构提取更多的代表性特征。更重要的是,我们进一步提出了一种简单而有效的多层次背景信息编码策略,以迎接肺结节大变异和难模拟的挑战。

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.

结果

在与ISBI2016相结合的LUNA16挑战中,我们提出的框架得到了广泛的验证,在那里我们在误报减少方面获得了最高的竞争绩效指标(CPM)评分。

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.

结论

实验结果证明了将多层次上下文信息整合到用于体积CT数据中的自动化肺结节检测的3-CNN框架中的重要性和有效性。

Conclusions

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.

意义

虽然我们的方法是针对肺部结节检测而设计的,但是所提出的框架是一般的,并且可以容易地扩展到来自容积医学图像的许多其他三维物体检测任务,其中目标物体具有大的变化并且伴随有许多硬模仿。

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.

阅读原文:PMID: 28113302  DOI: 10.1109/TBME.2016.2613502


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