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It remains challenging to automatically segment kidneys in clinicalultrasound (US) images due to the kidneys' varied shapes and image intensitydistributions, although semi-automatic methods have achieved promisingperformance. In this study, we propose subsequent boundary distance regressionand pixel classification networks to segment the kidneys, informed by the factthat the kidney boundaries have relatively homogenous texture patterns acrossimages. Particularly, we first use deep neural networks pre-trained forclassification of natural images to extract high-level image features from USimages, then these features are used as input to learn kidney boundary distancemaps using a boundary distance regression network, and finally the predictedboundary distance maps are classified as kidney pixels or non-kidney pixelsusing a pixel classification network in an end-to-end learning fashion. We alsoadopted a data-augmentation method based on kidney shape registration togenerate enriched training data from a small number of US images with manuallysegmented kidney labels. Experimental results have demonstrated that our methodcould effectively improve the performance of automatic kidney segmentation,significantly better than deep learning-based pixel classification networks.

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