21 March 2016 A neural network approach to lung nodule segmentation
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Abstract
Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%±0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.
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Yaoxiu Hu, Yaoxiu Hu, Prahlad G. Menon, Prahlad G. Menon, "A neural network approach to lung nodule segmentation", Proc. SPIE 9784, Medical Imaging 2016: Image Processing, 97842O (21 March 2016); doi: 10.1117/12.2217291; https://doi.org/10.1117/12.2217291
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