Projects:UtahAtlasSegmentation

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Atlas Based Segmentation

Automatic segmentation can be performed reliably using priors from brain atlases and an image generative model. We have developed a tool that provides an automatic segmentation pipeline in a modular framework. The processing pipeline is composed tasks such as filtering the input images, registering the multimodal input images and the brain atlas to a common space, followed by iterative steps which interleave segmentation, inhomogeneity correction, and atlas warping.

The tool is being integrated into Slicer as a plugin, and a screenshot of the prototype is shown below.

Screen shot of the segmentation plugin in Slicer.

Our tool generates bias corrected images, fuzzy classification maps, and discrete segmentation labels. The tool has been used to automatically segment thousands of adult and toddler images from the University of North Carolina (UNC), and is also being used as a skull stripping mechanism for DTI processing at UNC and Utah. An example of the output of the tool is shown below.

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Output of the segmentation plugin, showing the bias corrected image and the probabilities for white and gray matter.

Key Investigators

• Utah Algorithms: Marcel Prastawa, Guido Gerig