4.6 Article

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

期刊

BMC BIOINFORMATICS
卷 22, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12859-021-04202-8

关键词

Tissue clearing; Light sheet microscopy; Deep learning; Image segmentation; Manual annotation

资金

  1. NSF [ACI-16449916]
  2. NIH [R01MH121433, R01MH118349, R01MH120125, R01NS110791]
  3. Foundation of Hope
  4. Cancer Center Core Support Grant [P30 CA016086]
  5. North Carolina Biotech Center Institutional Support Grant [2016-IDG-1016]
  6. [P30 NS045892]

向作者/读者索取更多资源

Segmentor is an open-source tool designed for reliable, efficient, and user-friendly manual annotation and refinement of objects within 3D light sheet microscopy images, particularly nuclei. It employs a hybrid 2D-3D approach for visualizing and segmenting objects, with features for automatic region splitting to streamline the process of 3D segmentation. Editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy.
Background Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data. Results We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities. Conclusions Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at and .

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