Universal consensus 3D segmentation of cells from 2D segmented stacks | Nature Methods
u-Segment3D is a universal framework that translates and enhances 2D instance segmentations to a 3D consensus instance segmentation without training data. It performs well across diverse datasets, including cells with complex morphologies.
Download PDF Subjects Cellular imaging Image processing Machine learning Software Abstract Cell segmentation is the foundation of a wide range of microscopy-based biological studies. Deep learning has revolutionized two-dimensional (2D) cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However, three-dimensional (3D) cell segmentation, requiring dense annotation of 2D slices, still poses substantial challenges. Manual labeling of 3D cells to train broadly appl
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