Abstract
The estimates of traditional segmentation CNNs for the prediction of the follow-up tissue outcome in strokes are not yet accurate enough or capable of properly modeling the growth mechanisms of ischaemic stroke [1]. In our previous shape space interpolation approach [2], the prediction of the follow-up lesion shape has been bounded using core and penumbra segmentation estimates as priors. One of the challenges is to define well-suited growth constraints, as the transition from one to another shape may still result in a very unrealistic spatial evolution of the stroke.
| Original language | English |
|---|---|
| Title of host publication | Bildverarbeitung für die Medizin 2020 |
| Editors | Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm |
| Number of pages | 1 |
| Place of Publication | Wiesbaden |
| Publisher | Springer Vieweg, Wiesbaden |
| Publication date | 12.02.2020 |
| Pages | 143-143 |
| ISBN (Print) | 978-3-658-29266-9 |
| ISBN (Electronic) | 978-3-658-29267-6 |
| DOIs | |
| Publication status | Published - 12.02.2020 |
| Event | Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen - Berlin, Germany Duration: 15.03.2020 → 17.03.2020 Conference number: 237969 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 4 Quality Education
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
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SDG 12 Responsible Consumption and Production
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SDG 14 Life Below Water
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SDG 15 Life on Land
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
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