Article

CellSeg3D: self-supervised 3D cell segmentation for fluorescence microscopy

2024-05-17

Abstract excerpt

Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a set of models including a 3D transformer (SwinUNetR) and a novel 3D self-supervised learning method (WNet3D) designed to address the inherent complexity of generating 3D ground truth data and quantifying nuclei in 3D volumes. We developed a Python package...

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Literature Corpus work: 71513503-dd7b-5dde-b195-055c18fb5857
DOI: 10.1101/2024.05.17.594691

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