For the stable Slicer documentation, visit the 4.10 page.
Introduction and Acknowledgements
This work was partially funded by CAPES and CNPq, a Brazillian Agencies. Information on CAPES can be obtained on the CAPES website and CNPq website.
This extension provides image segmentation and local image contrast enhancement approaches in order to detect abnormal white matter voxels signals in magnetic resonance images. At moment, there are available the LS Segmenter (specific for hyperintense Multiple Sclerosis lesion segmentation on T2-FLAIR images), LS Contrast Enhancement (specific to increase the contrast of abnormal voxels of T2-FLAIR images) and AFT Segmenter as a simple implementation of a recent automatic Multiple Sclerosis (MS) lesion segmentation approaches. The LS Segmenter module implements a T2-FLAIR hyperintense lesion segmentation based on the algorithm published in the paper.
NOTE: The Logistic Contrast Enhancement, Weighted Enhancement Image Filter and Automatic FLAIR Threshold modules are only supporting CLI methods added in the extension in order to calculate specific parts of the segmentation procedure. For this reason, these modules are not supposed to be used alone. Please, use the LS Contrast Enhancer or LS Segmenter modules only.
Most frequently used for these scenarios:
- Use Case 1: Hyperintense Multiple Sclerosis (MS) lesions segmentation
- T2-FLAIR images are usually applied to MS diagnosis in order to detect hyperintense MS lesions. In this case, the LS Segmenter module can be useful.
- Use Case 2: Increase contrast in abnormal voxels in white matter tissue
- There are some lesion segmentation approaches that rely on the voxel intensity level presented in the white matter tissue, where the LS Contrast Enhancer module can be helpful to increase the contrast between lesions and surrounding brain tissues (mainly normal-appearing white matter - NAWM).
- da Silva Senra Filho, A. C. (2017) ‘A hybrid approach based on logistic classification and iterative contrast enhancement algorithm for hyperintense multiple sclerosis lesion segmentation’, Medical & Biological Engineering & Computing. doi: 10.1007/s11517-017-1747-2.
- Cabezas, M., Oliver, A., Roura, E., Freixenet, J., Vilanova, J. C., Ramió-Torrentà, L., Rovira, À. and Lladó, X. (2014) ‘Automatic multiple sclerosis lesion detection in brain MRI by FLAIR thresholding’, Computer Methods and Programs in Biomedicine. Elsevier Ireland Ltd, 115(3), pp. 147–161. DOI: 10.1016/j.cmpb.2014.04.006.
Information for Developers
|Section under construction.|
- DOI: 10.1016/j.cmpb.2014.04.006.
- DOI: 10.1007/s11517-017-1747-2