Towards Automatic Identification of Missing Tissues using a Geometric-Learning Correspondence Model Article Swipe
Eliana Vásquez Osorio
,
E HENDERSON
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.11265
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.11265
Missing tissue presents a big challenge for dose mapping, e.g., in the reirradiation setting. We propose a pipeline to identify missing tissue on intra-patient structure meshes using a previously trained geometric-learning correspondence model. For our application, we relied on the prediction discrepancies between forward and backward correspondences of the input meshes, quantified using a correspondence-based Inverse Consistency Error (cICE). We optimised the threshold applied to cICE to identify missing points in a dataset of 35 simulated mandible resections. Our identified threshold, 5.5 mm, produced a balanced accuracy score of 0.883 in the training data, using an ensemble approach. This pipeline produced plausible results for a real case where ~25% of the mandible was removed after a surgical intervention. The pipeline, however, failed on a more extreme case where ~50% of the mandible was removed. This is the first time geometric-learning modelling is proposed to identify missing points in corresponding anatomy.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.11265
- https://arxiv.org/pdf/2502.11265
- OA Status
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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- DOI
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https://doi.org/10.48550/arxiv.2502.11265Digital Object Identifier
- Title
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Towards Automatic Identification of Missing Tissues using a Geometric-Learning Correspondence ModelWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-02-16Full publication date if available
- Authors
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Eliana Vásquez Osorio, E HENDERSONList of authors in order
- Landing page
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https://arxiv.org/abs/2502.11265Publisher landing page
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https://arxiv.org/pdf/2502.11265Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2502.11265Direct OA link when available
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Identification (biology), Artificial intelligence, Computer science, Pattern recognition (psychology), Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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