Projects:PathologyAnalysis

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Description

Traumatic brain injury (TBI) occurs when an external force traumatically injures the brain. TBI is a major cause of death and disability worldwide, especially in children and young adults. TBI affects 1.4 million Americans annually. The UCLA medical school has been working on this topic for years.

On anatomical MRI scans, to quantitatively analyze the cortical thickness, white matter changes, we need to have a good segmentation on TBI images. However, for TBI data, standard automated image analysis methods are not robust with respect to the TBI-related changes in image contrast, changes in brain shape, cranial fractures, white matter fiber alterations, and other signatures of head injury.

We are working on an extension of the "atlas-based classification" method ABC [1] for TBI datasets with the clinical goal to efficiently segment healthy brain tissue and cerebral lesions. A main goal will be the automated segmentation of healthy brain tissue and user-assisted segmentation of various cerebral lesion types (hematoma, subarachnoid hemorrhage, contusion and DAI, perifocal (regional) to diffuse (generalized) edema, hemorrhagic diffuse axonal injury (DAI)and more. A strong emphasis will be on the joint of multiple imaging modalities (T1 pre- and T1 postcontrast, T2 (TSE), FLAIR, GRE, SWI, Perfusion, and DTI/DWI) for improved detection and quantitative characterization of lesion types.

Multimodal MRI data of TBI patient.

Experiments

We conducted experiments with the application of the ABC tool to multi-modal image data of 5 TBI cases provided by DBP partner UCLA.

Segmentation of 5channel TBI data with ABC. Lesions and ventricles segmented with additional 3D level-set segmentation.

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Segmentation of TBI data.

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Key Investigators

• Utah: Bo Wang, Marcel Prastawa, Guido Gerig
• UCLA: Jack Van Horn, Andrei Irimia, Micah Chambers

References

• ABC NA-MIC Project
• ABC nitrc
• Traumatic brain injury (TBI)