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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/194747
µMatch: 3D shape correspondence for biological image data
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Modern microscopy technologies allow imaging biological objects in 3D over a wide range of spatial and temporal scales, opening the way for a quantitative assessment of morphology. However, establishing a correspondence between objects to be compared, a first necessary step of most shape analysis workflows, remains challenging for soft-tissue objects without striking features allowing them to be landmarked. To address this issue, we introduce the μMatch 3D shape correspondence pipeline. μMatch implements a state-of-the-art correspondence algorithm initially developed for computer graphics and packages it in a streamlined pipeline including tools to carry out all steps from input data pre-processing to classical shape analysis routines. Importantly, μMatch does not require any landmarks on the object surface and establishes correspondence in a fully automated manner. Our open-source method is implemented in Python and can be used to process collections of objects described as triangular meshes. We quantitatively assess the validity of μMatch relying on a well-known benchmark dataset and further demonstrate its reliability by reproducing published results previously obtained through manual landmarking.
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KLATZOW, James, et al. µMatch: 3D shape correspondence for biological image data. Frontiers in Computer Science. 2022. Vol. 4. ISSN 2624-9898. [consulted: 11 of June of 2026]. Available at: https://hdl.handle.net/2445/194747