Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network.

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. PMID: 31803348 [PubMed]
Source: Proceedings - International Symposium on Biomedical Imaging - Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research