Language

• English | 中文 | 日本語

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.

Features

• 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.

• 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.

• Efficiency -- Seg2Link is engineered for the rapid processing of large 3D images with billions of voxels.

Graphic Overview

Install

• Install Anaconda or Miniconda
• Create a new conda environment and activate it by:

conda create -n seg2link-env python=3.8 pip
conda activate seg2link-env

• Install seg2link from this repository:

pip install git+https://github.com/Mohinta2892/Seg2Link.git

Editable install:

pip install -e git+https://github.com/Mohinta2892/Seg2Link.git#egg=Seg2Link

SPLIT ZARRS TO TIFF/ SAVED SEG.NPY INTO SLICES

Please use the scripts in utils to either split your zarrs into tiffs. Seg2Link ingests tiffs only. You can also split your saved segmentations into tiffs and used them. Generally Seg2Link should be able load a seg.npy file back after saving.

Use the software

• Activate the created environment by:

conda activate seg2link-env

• Start the software

seg2link

Citation

If 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

If you use this fork in your research, we would be grateful if you acknowledge it:

• Mohinta, S. https://github.com/Mohinta2892/Seg2Link.git