Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network
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DOI：
10.1109/tmi.2016.2535865
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发表时间：
2016-05-01
影响因子：
10.6
通讯作者：
Mougiakakou, Stavroula
中科院分区：
文献类型：
--
作者：
Anthimopoulos, Marios;Christodoulidis, Stergios;Mougiakakou, Stavroula
自动组织定征是间质性肺病（ILD）计算机辅助诊断（CAD）系统中最关键的组成部分之一。虽然在这一领域进行了大量的研究，但这个问题仍然具有挑战性。深度学习技术最近在各种计算机视觉问题上取得了令人印象深刻的成果，人们期望它们可以应用于其他领域，例如医学图像分析。在本文中，我们提出并评估了一个卷积神经网络（CNN），设计用于ILD模式的分类。拟议的网络由5个具有2 x 2内核和LeakyReLU激活的卷积层组成，然后是大小等于最终特征图大小的平均池化和三个密集层。最后一个致密层有7个输出，相当于所考虑的类别：健康、毛玻璃样混浊（GGO）、微结节、实变、网状、蜂窝和GGO/网状的组合。为了训练和评估CNN，我们使用了14696个图像块的数据集，这些图像块来自不同扫描仪和医院的120次CT扫描。据我们所知，这是第一个针对特定问题设计的深度CNN。一项比较分析证明了所提出的CNN在一个具有挑战性的数据集中对以前的方法的有效性。分类性能（类似于85.5%）证明了CNN在分析肺部模式方面的潜力。未来的工作包括，将CNN扩展到CT容积扫描提供的三维数据，并将所提出的方法集成到CAD系统中，该系统旨在为ILD提供鉴别诊断，作为放射科医生的支持工具。
Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2 x 2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (similar to 85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.