Conditional generative learning for medical image imputation Article Swipe
Ragheb Raad
,
Deep Ray
,
Bino Varghese
,
Darryl Hwang
,
Inderbir S. Gill
,
Vinay Duddalwar
,
Assad A. Oberai
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-50566-7
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-023-50566-7
Image imputation refers to the task of generating a type of medical image given images of another type. This task becomes challenging when the difference between the available images, and the image to be imputed is large. In this manuscript, one such application is considered. It is derived from the dynamic contrast enhanced computed tomography (CECT) imaging of the kidneys: given an incomplete sequence of three CECT images, we are required to impute the missing image. This task is posed as one of probabilistic inference and a generative algorithm to generate samples of the imputed image, conditioned on the available images, is developed, trained, and tested. The output of this algorithm is the “best guess” of the imputed image, and a pixel-wise image of variance in the imputation. It is demonstrated that this best guess is more accurate than those generated by other, deterministic deep-learning based algorithms, including ones which utilize additional information and more complex loss terms. It is also shown that the pixel-wise variance image, which quantifies the confidence in the reconstruction, can be used to determine whether the result of the imputation meets a specified accuracy threshold and is therefore appropriate for a downstream task.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-023-50566-7
- https://www.nature.com/articles/s41598-023-50566-7.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390500811
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390500811Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-023-50566-7Digital Object Identifier
- Title
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Conditional generative learning for medical image imputationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-02Full publication date if available
- Authors
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Ragheb Raad, Deep Ray, Bino Varghese, Darryl Hwang, Inderbir S. Gill, Vinay Duddalwar, Assad A. OberaiList of authors in order
- Landing page
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https://doi.org/10.1038/s41598-023-50566-7Publisher landing page
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https://www.nature.com/articles/s41598-023-50566-7.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-023-50566-7.pdfDirect OA link when available
- Concepts
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Imputation (statistics), Computer science, Artificial intelligence, Inference, Generative model, Probabilistic logic, Image (mathematics), Pixel, Pattern recognition (psychology), Missing data, Generative grammar, Machine learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
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2025: 6, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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