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 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Netherlands
Elsevier B.V
01.02.2020
Elsevier BV |
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| ISSN: | 1361-8415, 1361-8423, 1361-8423 |
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| Abstract | •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.
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| AbstractList | 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. 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.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. 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. •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] |
| ArticleNumber | 101602 |
| Author | Fischer, Katherine Zhang, Zhengqiang Furth, Susan L. Yin, Shi Li, Hongming Tasian, Gregory E. Peng, Qinmu You, Xinge Fan, Yong |
| AuthorAffiliation | Center for Pediatric Clinical Effectiveness, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA Department of Biostatistics, Epidemiology, and Informatics, The University of Pennsylvania, Philadelphia, PA, 19104, USA School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China Department of Pediatrics, Division of Pediatric Nephrology, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA Department of Surgery, Division of Pediatric Urology, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA |
| AuthorAffiliation_xml | – name: Department of Biostatistics, Epidemiology, and Informatics, The University of Pennsylvania, Philadelphia, PA, 19104, USA – name: Department of Pediatrics, Division of Pediatric Nephrology, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA – name: Department of Surgery, Division of Pediatric Urology, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA – name: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA – name: Center for Pediatric Clinical Effectiveness, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA – name: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China |
| Author_xml | – sequence: 1 givenname: Shi surname: Yin fullname: Yin, Shi organization: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: Qinmu surname: Peng fullname: Peng, Qinmu email: pengqinmu@hust.edu.cn organization: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China – sequence: 3 givenname: Hongming surname: Li fullname: Li, Hongming organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States – sequence: 4 givenname: Zhengqiang surname: Zhang fullname: Zhang, Zhengqiang organization: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China – sequence: 5 givenname: Xinge surname: You fullname: You, Xinge organization: School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China – sequence: 6 givenname: Katherine surname: Fischer fullname: Fischer, Katherine organization: Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States – sequence: 7 givenname: Susan L. surname: Furth fullname: Furth, Susan L. organization: Department of Pediatrics, Division of Pediatric Nephrology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States – sequence: 8 givenname: Gregory E. surname: Tasian fullname: Tasian, Gregory E. organization: Department of Surgery, Division of Pediatric Urology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States – sequence: 9 givenname: Yong orcidid: 0000-0001-9869-4685 surname: Fan fullname: Fan, Yong email: yong.fan@ieee.org organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31760193$$D View this record in MEDLINE/PubMed |
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| Keywords | Boundary detection Ultrasound images Pixelwise classification Boundary distance regression |
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| Snippet | •A fully automatic segment method for clinical ultrasound kidney images.•End-to-end learning of boundary detection and pixelwise classification... It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions,... It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys’ varied shapes and image intensity distributions,... |
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| SubjectTerms | 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 |
| Title | Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks |
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