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

•A fully automatic segment method for clinical ultrasound kidney images.•End-to-end learning of boundary detection and pixelwise classification networks.•Achieved significantly better performance than pixelwise classification networks.•Data-augment improved the segmentation performance. It remains c...

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Veröffentlicht in:Medical image analysis Jg. 60; S. 101602
Hauptverfasser: Yin, Shi, Peng, Qinmu, Li, Hongming, Zhang, Zhengqiang, You, Xinge, Fischer, Katherine, Furth, Susan L., Tasian, Gregory E., Fan, Yong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Netherlands Elsevier B.V 01.02.2020
Elsevier BV
Schlagworte:
Aged, 80 and over
Artificial neural networks
Boundary detection
Boundary distance regression
Classification
Datasets as Topic
Deep Learning
Feature extraction
Female
Humans
Image classification
Image processing
Image segmentation
Kidney Diseases - diagnostic imaging
Kidneys
Machine learning
Male
Neural networks
Neural Networks, Computer
Pattern Recognition, Automated
Pixels
Pixelwise classification
Regression
Ultrasonic imaging
Ultrasonography - methods
Ultrasound
Ultrasound images
Boundary detection
Ultrasound images
Pixelwise classification
Boundary distance regression
ISSN:1361-8415, 1361-8423, 1361-8423
Online-Zugang:Volltext
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Zusammenfassung:•A fully automatic segment method for clinical ultrasound kidney images.•End-to-end learning of boundary detection and pixelwise classification networks.•Achieved significantly better performance than pixelwise classification networks.•Data-augment improved the segmentation performance. 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. [Display omitted]
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ISSN:1361-8415
1361-8423
1361-8423
DOI:10.1016/j.media.2019.101602