Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation. Article Swipe
Minyoung Chung
,
Jingyu Lee
,
Min-Kyung Lee
,
Jeongjin Lee
,
Yeong-Gil Shin
·
YOU?
·
· 2018
· Open Access
·
YOU?
·
· 2018
· Open Access
·
Objective: Herein, a neural network-based liver segmentation algorithm is proposed, and its performance was evaluated using abdominal computed tomography (CT) images. Methods: A fully convolutional network was developed to overcome the volumetric image segmentation problem. To guide a neural network to accurately delineate a target liver object, the network was deeply supervised by applying the adaptive self-supervision scheme to derive the essential contour, which acted as a complement with the global shape. The discriminative contour, shape, and deep features were internally merged for the segmentation results. Results and Conclusion: 160 abdominal CT images were used for training and validation. The quantitative evaluation of the proposed network was performed through an eight-fold cross-validation. The result showed that the method, which uses the contour feature, segmented the liver more accurately than the state-of-the-art with a 2.13% improvement in the dice score. Significance: In this study, a new framework was introduced to guide a neural network and learn complementary contour features. The proposed neural network demonstrates that the guided contour features can significantly improve the performance of the segmentation task.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://arxiv.org/abs/1808.00739v1
- OA Status
- green
- Cited By
- 3
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W2886435763
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2886435763Canonical identifier for this work in OpenAlex
- Title
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Deeply Self-Supervising Edge-to-Contour Neural Network Applied to Liver Segmentation.Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-02Full publication date if available
- Authors
-
Minyoung Chung, Jingyu Lee, Min-Kyung Lee, Jeongjin Lee, Yeong-Gil ShinList of authors in order
- Landing page
-
https://arxiv.org/abs/1808.00739v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/abs/1808.00739v1Direct OA link when available
- Concepts
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Artificial intelligence, Segmentation, Computer science, Pattern recognition (psychology), Convolutional neural network, Discriminative model, Artificial neural network, Feature (linguistics), Computer vision, Image segmentation, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2022: 1, 2020: 1Per-year citation counts (last 5 years)
- Related works (count)
-
20Other works algorithmically related by OpenAlex
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