Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation Article Swipe
Hong-Yu Zhou
,
Hualuo Liu
,
Shilei Cao
,
Dong Wei
,
Chixiang Lu
,
Yizhou Yu
,
Kai Ma
,
Yefeng Zheng
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.16344
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.16344
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic. We propose pyramid reasoning modules (PRMs) to model the anatomical correlation between anchor and target volumes. In practice, the proposed module first computes a correlation matrix between target and anchor computerized tomography (CT) volumes. Then, this matrix is used to transform the feature representations of both anchor volume and its segmentation mask. Finally, OrganNet learns to fuse the representations from various inputs and predicts segmentation results for target volume. Extensive experiments show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task. Moreover, even when compared with fully-supervised segmentation models, OrganNet is still able to produce satisfying segmentation results.
Related Topics
Concepts
Segmentation
Artificial intelligence
Computer science
Pyramid (geometry)
Process (computing)
Task (project management)
Pattern recognition (psychology)
Fuse (electrical)
Feature (linguistics)
Computer vision
Machine learning
Mathematics
Economics
Philosophy
Geometry
Management
Electrical engineering
Operating system
Engineering
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.16344
- https://arxiv.org/pdf/2103.16344
- OA Status
- green
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3148164993
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3148164993Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2103.16344Digital Object Identifier
- Title
-
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical CorrelationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-30Full publication date if available
- Authors
-
Hong-Yu Zhou, Hualuo Liu, Shilei Cao, Dong Wei, Chixiang Lu, Yizhou Yu, Kai Ma, Yefeng ZhengList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.16344Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2103.16344Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2103.16344Direct OA link when available
- Concepts
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Segmentation, Artificial intelligence, Computer science, Pyramid (geometry), Process (computing), Task (project management), Pattern recognition (psychology), Fuse (electrical), Feature (linguistics), Computer vision, Machine learning, Mathematics, Economics, Philosophy, Geometry, Management, Electrical engineering, Operating system, Engineering, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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