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Segmentor: a tool for manual refinement of 3D microscopy annotations.

David Borland1, Carolyn M McCormick2,3, Niyanta K Patel2,3

  • 1RENCI, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 540, Chapel Hill, NC, 27517, USA.

BMC Bioinformatics
|May 23, 2021
PubMed
Summary
This summary is machine-generated.

Segmentor streamlines manual annotation of 3D microscopy images, improving efficiency for training deep learning models. This open-source tool enhances the segmentation of nuclei in large datasets, accelerating biological research.

Keywords:
Deep learningImage segmentationLight sheet microscopyManual annotationTissue clearing

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Area of Science:

  • Neuroscience
  • Biotechnology
  • Computational Biology

Background:

  • Advanced tissue clearing and light sheet microscopy enable rapid 3D imaging of biological specimens like whole mouse brains.
  • Quantitative analysis of 3D images is crucial for understanding structure-function relationships in the brain.
  • Accurate segmentation of dense structures, such as nuclei, is challenging but essential for deep learning models.

Purpose of the Study:

  • To develop an efficient and user-friendly tool for manual annotation and refinement of objects in 3D microscopy images.
  • To facilitate the training of deep learning-based nuclear segmentation algorithms.

Main Methods:

  • Introduction of Segmentor, an open-source tool for manual annotation of 3D light sheet microscopy images.
  • Utilizes a hybrid 2D-3D approach for object visualization and segmentation.
  • Incorporates automatic region splitting features to optimize 3D segmentation workflows.

Main Results:

  • Segmentor significantly reduces the time required for manual annotation compared to 2D-only editing.
  • Simultaneous 2D and 3D editing in Segmentor maintains annotation accuracy.
  • The tool is specifically designed to streamline the segmentation of nuclei in 3D datasets.

Conclusions:

  • Segmentor enhances the efficiency of manual annotation and refinement for 3D objects.
  • The tool aids in generating high-quality training data for deep learning segmentation algorithms.
  • Segmentor is freely available for use and development.