Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

TitleAutomatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.
Publication TypeJournal Article
Year of Publication2019
AuthorsYin S, Peng Q, Li H, Zhang Z, You X, Fischer K, Furth SL, Tasian GE, Fan Y
JournalMed Image Anal
Volume60
Pagination101602
Date Published2019 Nov 08
ISSN1361-8423
Abstract

It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we propose subsequent boundary distance regression and pixel classification networks to segment the kidneys automatically. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from US images. These features are used as input to learn kidney boundary distance maps using a boundary distance regression network and the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixelwise classification network in an end-to-end learning fashion. We also adopted a data-augmentation method based on kidney shape registration to generate enriched training data from a small number of US images with manually segmented kidney labels. Experimental results have demonstrated that our method could automatically segment the kidney with promising performance, significantly better than deep learning-based pixel classification networks.

DOI10.1016/j.media.2019.101602
Alternate JournalMed Image Anal
PubMed ID31760193