Accurate segmentation of lung fields on chest radiographs is the primary step for computer-aided detection of various
conditions such as lung cancer and tuberculosis. The size, shape and texture of lung fields are key parameters for chest
X-ray (CXR) based lung disease diagnosis in which the lung field segmentation is a significant primary step. Although
many methods have been proposed for this problem, lung field segmentation remains as a challenge. In recent years,
deep learning has shown state of the art performance in many visual tasks such as object detection, image classification
and semantic image segmentation. In this study, we propose a deep convolutional neural network (CNN) framework for
segmentation of lung fields. The algorithm was developed and tested on 167 clinical posterior-anterior (PA) CXR images
collected retrospectively from picture archiving and communication system (PACS) of Geisinger Health System. The
proposed multi-scale network is composed of five convolutional and two fully connected layers. The framework
achieved IOU (intersection over union) of 0.96 on the testing dataset as compared to manual segmentation. The
suggested framework outperforms state of the art registration-based segmentation by a significant margin. To our
knowledge, this is the first deep learning based study of lung field segmentation on CXR images developed on a
heterogeneous clinical dataset. The results suggest that convolutional neural networks could be employed reliably for
lung field segmentation.
Mohammad R. Arbabshirani, Ahmed H. Dallal, Chirag Agarwal, Aalpan Patel, and Gregory Moore, "Accurate segmentation of lung fields on chest radiographs using deep convolutional networks," Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013305 (Presented at SPIE Medical Imaging: February 12, 2017; Published: 24 February 2017); https://doi.org/10.1117/12.2254526.
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