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3D Segmentation of Mouse Brain Cell Nuclei Imaged Using Light Sheet Microscopy| title | 3D Segmentation of Mouse Brain Cell Nuclei Imaged Using Light Sheet Microscopy |
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| start_date | 2024/05/31 |
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| schedule | 14h |
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| online | no |
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| location_info | TPR2 salle de réunion 04.03 |
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| summary | Medical image segmentation has been a widely studied field in the computer vision community. Several methods have been developed using both conventional and deep learning approaches. Among deep learning methods, U-Net is still one of the most used architectures, based on the use of a convolutional auto-encoder. Several modalities need to be tackled when dealing with biological image processing. We focus our study on two important aspects: the need for annotated datasets to train a neural network, and the need to perform 3D segmentation.
Light Sheet Fluorescence Microscopy provides high-quality images of neural circuitry, enabling the scanning of an entire mouse brain quickly. Analyzing the cell nuclei and the whole neural structure is a challenging task, involving the automatic extraction of both cell nuclei and neurons. Neurons can have a wide variety of shapes and are better captured using 3D imaging. Cell nuclei segmentation in the case of wide-field microscopy is still a challenging task, as the size of nuclei can sometimes be only a couple of pixels. These nuclei can also aggregate, making it even more difficult to differentiate them. |
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| responsibles | NC |
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Workflow history| from state (1) | to state | comment | date |
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| submitted | published | | 2024/05/31 10:36 UTC |
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