Seg2Link is a napari-based software specifically designed for scientific research. The software aims to tackle a focused problem: offering an efficient toolbox for quick manual refinement of automated segmentation in large-scale 3D cellular images, particularly useful for brain images obtained through electron microscopy."
Our extensive documentation offers step-by-step tutorials, and our academic paper delves into the scientific methodology and validation behind the software.
Unlike other segmentation solutions, Seg2Link requires pre-processed predictions of cell/non-cell regions as inputs. These predictions can conveniently be generated using Seg2linkUnet2d (Documentation). This integrated approach makes the segmentation process both accurate and efficient.
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Utilizing Deep Learning Predictions -- Seg2Link takes deep learning predictions as input and refines initial inaccurate predictions into highly accurate results through semi-automatic user operations.
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User-Friendly -- Seg2Link not only auto-generates segmentation results but also allows for easy inspection and manual corrections through minimal mouse and keyboard interactions. It supports features like cell ordering, multiple-step undo and redo.
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Efficiency -- Seg2Link is engineered for the rapid processing of large 3D images with billions of voxels.
$ conda create -n seg2link-env python=3.8 pip
$ conda activate seg2link-env- Install seg2link:
$ pip install seg2link- Update to the latest version:
$ pip install --upgrade seg2link- Activate the created environment by:
$ conda activate seg2link-env- Start the software
$ seg2linkIf you used this package in your research please cite it:
- Wen, C., Matsumoto, M., Sawada, M. et al. Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks. Sci Rep 13, 7109 (2023). https://doi.org/10.1038/s41598-023-34232-6