Efficient labeling for fine‐tuning chest X‐ray bone‐suppression networks for pediatric patients

作者：Weijie Xie, Mengkun Gan, Xiao Tan, Mujiao Li, Wei Yang, Wenhui Wang · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17516 · 被引用次数：4 · 研究领域：COVID-19 diagnosis using AI、Advanced X-ray and CT Imaging、Medical Imaging and Analysis

BACKGROUND: Pneumonia, a major infectious cause of morbidity and mortality among children worldwide, is typically diagnosed using low-dose pediatric chest X-ray [CXR (chest radiography)]. In pediatric CXR images, bone occlusion leads to a risk of missed diagnosis. Deep learning-based bone-suppression networks relying on training data have enabled considerable progress to be achieved in bone suppression in adult CXR images; however, these networks have poor generalizability to pediatric CXR images because of the lack of labeled pediatric CXR images (i.e., bone images vs. soft-tissue images). Dual-energy subtraction imaging approaches are capable of producing labeled adult CXR images; however, their application is limited because they require specialized equipment, and they are infrequently employed in pediatric settings. Traditional image processing-based models can be used to label pediatric CXR images, but they are semiautomatic and have suboptimal performance. PURPOSE: We developed an efficient labeling approach for fine-tuning pediatric CXR bone-suppression networks capable of automatically suppressing bone structures in CXR images for pediatric patients without the need for specialized equipment and technologist training. METHODS: Three steps were employed to label pediatric CXR images and fine-tune pediatric bone-suppression networks: distance transform-based bone-edge detection, traditional image processing-based bone suppression, and fully automated pediatric bone supp...