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Image-based quantitative morphology with geometrical models

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Images and video sequences are crucial sources of information in experimental biology, and quantitative bioimage analysis has grown into an independent field of research. Recent advances in computer vision provide efficient learning-based tools to identify objects in images, as well as segment and track them. Classically, objects in an image are then defined by the set of pixels or voxels that compose them, which is ill-suited to capture many of the subtle features that are typical of biological phenomenon. While the process of image acquisition acts as an analog-to-digital converter, projecting the world onto the space of image data, geometrical modelling can be used to revert this effect and represent biological object from images in an “infinite resolution” way. Uhlmann group focuses on the development of such models to offer a common, unified mathematical representation of morphology, regardless of the imaging modality. Ultimately, it allows studying phenotypic features, as well as changes and differences thereof, in a purely analytical way. In her talk Dr Uhlmann will describe the general principles behind the construction of geometrical models in 2 and 3D and illustrate their use in various bioimage analysis problems

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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