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Medical Image Registration for Glioblastoma MRI Using Constrained Metamorphosis| title | Medical Image Registration for Glioblastoma MRI Using Constrained Metamorphosis |
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| start_date | 2024/06/04 |
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| schedule | 15h-16h |
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| online | no |
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| location_info | Salle 314 |
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| summary | Registering three-dimensional medical images presents inherent challenges, exacerbated by topological variations between the images. Even the latest state-of-the-art methods often struggle to achieve realistic matching under these conditions. My research addresses these challenges by focusing on the registration of glioblastoma brain MRI, encompassing configurations such as healthy to cancerous states and post-operative scenarios.To tackle this task, we implemented Metamorphosis and LDDMM for 2D and 3D images using an object-oriented approach in PyTorch, with GPU acceleration and a semi-Lagrangian scheme. However, the classical Metamorphosis framework did not yield satisfactory results. To address this, we extended the framework to incorporate prior knowledge, which we term Constrained Metamorphosis. This extension allows for the addition of constraints to the registration problem by matching given priors, specifically a growing mask from a given segmentation and a field guiding deformation in a desired direction. We demonstrate the effectiveness of our approach through experiments on glioblastomas using BraTS datasets, comparing our results with state-of-the-art methods. In conclusion, I will discuss ongoing projects. |
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| responsibles | Vacher, Blusseau |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2024/05/30 12:50 UTC |
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