1 research outputs found
Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network
It remains challenging to automatically segment kidneys in clinical
ultrasound images due to the kidneys' varied shapes and image intensity
distributions, although semi-automatic methods have achieved promising
performance. In this study, we developed a novel boundary distance regression
deep neural network to segment the kidneys, informed by the fact that the
kidney boundaries are relatively consistent across images in terms of their
appearance. Particularly, we first use deep neural networks pre-trained for
classification of natural images to extract high-level image features from
ultrasound images, then these feature maps are used as input to learn kidney
boundary distance maps using a boundary distance regression network, and
finally the predicted boundary distance maps are classified as kidney pixels or
non-kidney pixels using a pixel classification network in an end-to-end
learning fashion. Experimental results have demonstrated that our method could
effectively improve the performance of automatic kidney segmentation,
significantly better than deep learning based pixel classification networks.Comment: 4 pages. arXiv admin note: substantial text overlap with
arXiv:1811.0481