Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels
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Pathological lung segmentation based on random forest combined with deep model and multi-scale superpixels Caixia Liu1 · Ruibin Zhao1 · Wangli Xie1 · Mingyong Pang1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Accurate segmentation of lungs in pathological thoracic computed tomography (CT) scans plays an important role in pulmonary disease diagnosis. However, it is still a challenging task due to the variability of pathological lung appearances and shapes. In this paper, we proposed a novel segmentation algorithm based on random forest (RF), deep convolutional network, and multi-scale superpixels for segmenting pathological lungs from thoracic CT images accurately. A pathological thoracic CT image is first segmented based on multi-scale superpixels, and deep features, texture, and intensity features extracted from superpixels are taken as inputs of a group of RF classifiers. With the fusion of classification results of RFs by a fractional-order gray correlation approach, we capture an initial segmentation of pathological lungs. We finally utilize a divide-and-conquer strategy to deal with segmentation refinement combining contour correction of left lungs and region repairing of right lungs. Our algorithm is tested on a group of thoracic CT images affected with interstitial lung diseases. Experiments show that our algorithm can achieve a high segmentation accuracy with an average DSC of 96.45% and PPV of 95.07%. Compared with several existing lung segmentation methods, our algorithm exhibits a robust performance on pathological lung segmentation. Our algorithm can be employed reliably for lung field segmentation of pathologic thoracic CT images with a high accuracy, which is helpful to assist radiologists to detect the presence of pulmonary diseases and quantify its shape and size in regular clinical practices. Keywords Pathological lung segmentation · Convolutional neural network · Random forest · Divide-and-conquer strategy
1 Introduction Pulmonary disease is one of the major causes of morbidity and mortality around the world [1,2]. For example, the recent global outbreak of COVID-19 has killed tens of thousands of people in just a few months. Early diagnosis of pulmonary disease with computed tomography (CT) technique is crucial for making treatment decisions. In non-invasive detection
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Mingyong Pang [email protected] Institute of EduInfo Science and Engineering, Nanjing Normal University, Nanjing, China
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Fig. 1 Pathological lung segmentation. A pathological thoracic CT image (a) with its segmented lung mask (b)
and diagnosis of pulmonary disease, accurate lung segmentation is often a prerequisite for assessing the disease severity, it ensures that disease detection is not confounded by regions outside lungs [2]. However, inner structures of thoracic CT images are usually various with different textures and pixel densities. Additionally, intensities of pathological images are inhomogeneous and it is difficult to provide a reliable generic solution fo
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