Accurate and automatic lung nodule segmentation is of prime importance for the lung cancer analysis and its fundamental step in computer-aided diagnosis (CAD) systems. However, various types of nodule and visual similarity with its surrounding chest region make it challenging to develop lung nodule segmentation algorithm. In this paper, we proposed the Deep Deconvolutional Residual Network (DDRN) based approach for the lung nodule segmentation from the CT images. Our approach is based on two key insights. Proposed deep deconvolutional residual network trained end to end and captures the diverse variety of the nodules from the 2D set of the CT images. Summation-based long skip connection from convolutional to deconvolutional part of the network preserves the spatial information lost during the pooling operation and captures the full resolution features. The proposed method is evaluated on the publicly available Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) dataset. Results indicate that our proposed method can successfully segment nodules and achieve the average Dice scores of 94.97%, and Jaccard index of 88.68%.

中文翻译：

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基于深度反卷积残差网络的自动肺结节分割。

准确和自动的肺结节分割对于肺癌分析及其在计算机辅助诊断 (CAD) 系统中的基本步骤至关重要。然而，各种类型的结节以及与其周围胸部区域的视觉相似性使得开发肺结节分割算法具有挑战性。在本文中，我们提出了基于深度反卷积残差网络 (DDRN) 的方法，用于从 CT 图像中分割肺结节。我们的方法基于两个关键见解。提出的深度反卷积残差网络端到端训练并从 CT 图像的 2D 集中捕获各种结节。从网络的卷积部分到反卷积部分的基于求和的长跳跃连接保留了池化操作期间丢失的空间信息并捕获了全分辨率特征。所提出的方法是在公开可用的肺影像数据库联盟和影像数据库资源倡议 (LIDC/IDRI) 数据集上进行评估的。结果表明，我们提出的方法可以成功分割结节，平均 Dice 得分为 94.97%，Jaccard 指数为 88.68%。