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.
Author: Antonio Carlos da S. Senra Filho, CSIM Laboratory (University of Sao Paulo, Department of Computing and Mathematics)
Contact: Antonio Carlos da S. Senra Filho <email>acsenrafilho@usp.br</email>

CSIM Laboratory

University of Sao Paulo

CNPq Brazil

CAPES Brazil

Extension Description

Brain tissue segmentation plays an important role in many different image processing steps, which offers a possibility to study different neurodegenerative diseases or also in more specific tasks such as in brain structural quantitative parameters (brain atrophy, for instance). For this reason, brain tissue segmentation procedures have been intensively studied in recent years. This extension aims to offer a fast, robust and simple solutions for brain tissue segmentation, in which specific segmentation tasks are separated in different modules. See the modules list below to find the most appropriate tool for your study.

Modules

General purpose segmentation method: Brain Structures Segmenter
Global brain tissues segmentation: Basic Brain Tissues

NOTE: The Brain Structures Segmenter is the main module for a general brain segmentation approach, which uses a predefined image processing pipeline in order to result in a smoothed tissue mask. See the module's documentation for more details.

Use Cases

Most frequently used for these scenarios:

Use Case 1: Tissue classification
- There are several image quantitative approaches that are applied only in a certain tissue type (for instance, cortical thickness) in which a previous brain segmentation could be needed.
Use Case 2: Restrict brain regions for image quantitative analysis
- A white matter mask could be used for specific measurements in some brain diseases, for example, in Multiple Sclerosis it could be useful to help lesion detection algorithms.