2D 3D interactive multi-label image segmentation

version 2.0.0 (4.92 MB) by Xin Chen
Interactive image segmentation tool that provides efficient segmentation of multiple labels for both 2D and 3D medical images.

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Updated 19 Aug 2022

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Our developed interactive image segmentation tool provides efficient segmentation of multiple labels for both 2D and 3D medical images. The core segmentation method is based on a fast implementation of the fully connected conditional random field The software also enables automatic recommendation of the next slice to be annotated in 3D, leading to a higher efficiency.
Data format: most 2D image format and .mat, nifti and DICOM for 3D medical images.
Please read readme.txt in the folder.

Cite As

Xin Chen (2022). 2D 3D interactive multi-label image segmentation (https://www.mathworks.com/matlabcentral/fileexchange/113415-2d-3d-interactive-multi-label-image-segmentation), MATLAB Central File Exchange. Retrieved .

Li, Ruizhe, and Xin Chen. “An Efficient Interactive Multi-Label Segmentation Tool for 2D and 3D Medical Images Using Fully Connected Conditional Random Field.” Computer Methods and Programs in Biomedicine, vol. 213, Elsevier BV, Jan. 2022, p. 106534, doi:10.1016/j.cmpb.2021.106534.

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MATLAB Release Compatibility
Created with R2022a
Compatible with R2021a to R2021b
Platform Compatibility
Windows macOS Linux

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