24 February 2017 Accurate segmentation of lung fields on chest radiographs using deep convolutional networks
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Abstract
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.
Conference Presentation
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Mohammad R. Arbabshirani, Mohammad R. Arbabshirani, Ahmed H. Dallal, Ahmed H. Dallal, Chirag Agarwal, Chirag Agarwal, Aalpan Patel, Aalpan Patel, Gregory Moore, Gregory Moore, } "Accurate segmentation of lung fields on chest radiographs using deep convolutional networks", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013305 (24 February 2017); doi: 10.1117/12.2254526; https://doi.org/10.1117/12.2254526
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