A general method for bootstrapping dense 3D segmentations from sparse 2D annotations

Abstract excerpt

Analyzing volumetric microscopy data requires dense segmentation, yet training accurate machine learning models demands prohibitively expensive 3D ground-truth. To overcome this bottleneck, we present a lightweight framework that bootstraps dense 3D instance segmentations directly from sparse 2D annotations. A 2D network trained on sparse labels predicts complete boundaries on every section, and a 3D network pre-t...

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• Caenorhabditis elegans
• Deep Learning
• Image Processing, Computer-Assisted
• Lipid Droplets
• Microtubules
• Mutation
• Neural Networks, Computer
• tau Proteins

Identifiers and source

Literature Corpus work

a3bd7eb9-8a60-5975-840b-e637e26f33fb

DOI

10.1101/2024.06.14.599135