Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset

• Published in: Biomedical engineering online Vol. 18; no. 1; pp. 2 - 21
• Main Authors: Xu, Mingjie, Qi, Shouliang, Yue, Yong, Teng, Yueyang, Xu, Lisheng, Yao, Yudong, Qian, Wei
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.01.2019; BioMed Central; BMC
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; CAT scans; Chronic obstructive pulmonary disease; Classification; Cluster analysis; Clustering; Computed tomography; Convolutional neural network; Datasets; Diagnosis; Diseases; Image processing; Image segmentation; International conferences; Learning algorithms; Lesions; Lung cancer; Lung diseases; Lung parenchyma; Lungs; Machine learning; Medical diagnosis; Medical imaging; Medical treatment; Methods; Neural networks; Obstructive lung disease; Optimization; Parenchyma; Patches (structures); Positron emission tomography; Scanners; Segmentation; Training; Vector quantization; China; CT; Segmentation; Clustering; Lung parenchyma; Convolutional neural network

Lung segmentation constitutes a critical procedure for any clinical-decision supporting system aimed to improve the early diagnosis and treatment of lung diseases. Abnormal lungs mainly include lung parenchyma with commonalities on CT images across subjects, diseases and CT scanners, and lung lesion...