ArticleBMC bioinformatics2021
Segmentor: a tool for manual refinement of 3D microscopy annotations.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- CellPheno: A High-throughput Computational Platform for Quantifying Cellular Resolution Whole Brain Microscopy Images.bioRxiv : the preprint server for biology · 2026Article
- Article
- Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.Journal of cell science · 2024Article
- Rigor and reproducibility in human brain organoid research: Where we are and where we need to go.Stem cell reports · 2024Review
- A multiparametric analysis including single-cell and subcellular feature assessment reveals differential behavior of spheroid cultures on distinct ultra-low attachment plate types.Frontiers in bioengineering and biotechnology · 2024Article
- NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images.Scientific reports · 2023Article
- Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose.Cells & development · 2022Article
- Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy.Journal of visualized experiments : JoVE · 2022Article
- Tutorial: methods for three-dimensional visualization of archival tissue material.Nature protocols · 2021Review
- NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images.Cell reports · 2021Article
- Deep learning for bioimage analysis in developmental biology.Development (Cambridge, England) · 2021Review
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Authors and funding
12 authors.
Funding
Abstract
backgroundRecent 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.
resultsWe 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.
conclusionsSegmentor 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 https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .
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