Automatic kidney segmentation in 3D pediatric ultrasound images using deep neural networks and weighted fuzzy active shape model

Abstract

Automatic kidney segmentation in 3D ultrasound (3DUS) images is clinically important to provide a fast and reliable diagnosis of diseased kidneys. US imaging is a challenging modality for organ evaluation, especially for pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic kidney segmentation method in pediatric 3DUS images using the combination of deep neural networks and weighted fuzzy active shape model. We used deep neural networks to localize the kidney bounding box. The box was then used to initialize the weighted fuzzy active shape model and complete the fully automatic segmentation of the kidney capsule in 3DUS. The performance of the method was evaluated using a dataset of 45 kidneys, showing an average Dice similarity score of 0.82 ± 0.06 and average symmetric surface distance of 1.94 ± 0.74 mm.