Pulmonary Nodule Segmentation Using Deep Learning: A Review

Abstract

Accurate segmentation of pulmonary nodule within medical imagery is of great significance for classification and diagnosis. This task is profoundly challenging due to scarcity of annotated data, the diversity of lung nodule features, and the complexity of the background, making it very difficult to accurately and automatically segment lung nodules. Fortunately, deep learning is widely used in the field of pulmonary nodule segmentation and has achieved good and promising results. In this study, we conducted a comprehensive review of pulmonary nodule segmentation in scholarly journals and conferences within the domains of engineering and computer science over the past five years. Our focus was on the application of various deep-learning architectures for pulmonary nodule segmentation and the exploration of diverse deep learning models tailored to the unique challenges posed by this task. We summarized the pivotal methodologies underpinning lung nodule segmentation based on deep learning, including data preprocessing, network architecture, and loss function. Additionally, we discussed the advantages and disadvantages of different models. Finally, we identified research gaps and outlined future directions in the field of deep learning-driven pulmonary nodule image segmentation, aiming to provide researchers with a comprehensive evaluation of recent studies.

Keywords

• Computed tomography
• deep learning
• image preprocessing
• pulmonary nodule segmentation