Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales
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Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales Yupeng Xu 1
&
Yi Zhang 2 & Ke Bi 3 & Zhiyu Ning 4 & Lisha Xu 2 & Mengjun Shen 2 & Guoying Deng 5 & Yin Wang 2
# Society for Imaging Informatics in Medicine 2020
Abstract To evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Average Symmetric Surface Distance (ASSD), and Maximum symmetric surface distance (MSSD) was assessed. Our dual-stage boundary-restored network (BRN) outperformed existing segmentation methods (U-Net and a fully convolutional network (FCN)) for the segmentation accuracy parameters including DSC (83.45 ± 16.60%), MCC (0.8330 ± 0.1626), Jaccard (0.7391 ± 0.1770), ASSD (5.68 ± 2.70 mm), and MSSD (15.61 ± 6.07 mm). It also outperformed the original BRN in terms of the DSC by almost 5%. Our results suggest that deep learning algorithms aid fully automated SPL segmentation in patients with SPLs. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of lung US imaging. Keywords Convolutional neural network (CNN) . Deep learning . Image segmentation . Subpleural pulmonary lesion (SPL) segmentation . Ultrasound image
Introduction Y.X., Y.Z., and K.B. contributed equally to this manuscript and thus should be considered as co-first authors * Guoying Deng [email protected] * Yin Wang [email protected] 1
Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai 200080, China
2
Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
3
Department of Pathology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200092, China
4
School of Computer Science, The University of Sydney, Sydney, NSW 2006, Australia
5
Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China
Subpleural pulmonary lesions (SPLs) are focal opacities with a very high pretest probability of malignancy [1]. SPLs can be primary malignant or benign pulmonary lesions, including cancer, pneumonia, and tuberculosis. Lung cancer is one of the most frequently diagnosed tumors globally and has the highest morbidity and mortality rates
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