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

Abstract

BackgroundPneumonia, 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.PurposeWe 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.MethodsThree 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 suppression. In distance transform–based bone‐edge detection, bone edges were automatically detected by predicting bone‐edge distance‐transform images, which were then used as inputs in traditional image processing. In this processing, pediatric CXR images were labeled by obtaining bone images through a series of traditional image processing techniques. Finally, the pediatric bone‐suppression network was fine‐tuned using the labeled pediatric CXR images. This network was initially pretrained on a public adult dataset comprising 240 adult CXR images (A240) and then fine‐tuned and validated on 40 pediatric CXR images (P260_40labeled) from our customized dataset (named P260) through five‐fold cross‐validation; finally, the network was tested on 220 pediatric CXR images (P260_220unlabeled dataset).ResultsThe distance transform–based bone‐edge detection network achieved a mean boundary distance of 1.029. Moreover, the traditional image processing–based bone‐suppression model obtained bone images exhibiting a relative Weber contrast of 93.0%. Finally, the fully automated pediatric bone‐suppression network achieved a relative mean absolute error of 3.38%, a peak signal‐to‐noise ratio of 35.5 dB, a structural similarity index measure of 98.1%, and a bone‐suppression ratio of 90.1% on P260_40labeled.ConclusionsThe proposed fully automated pediatric bone‐suppression network, together with the proposed distance transform–based bone‐edge detection network, can automatically acquire bone and soft‐tissue images solely from CXR images for pediatric patients and has the potential to help diagnose pneumonia in children.