Brain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach

TitleBrain Extraction from Normal and Pathological Images: A Joint PCA/Image-Reconstruction Approach
Publication TypeJournal Article
Year of Publication2017
AuthorsHan X., Kwitt R, Aylward S, Menze B, Asturias A., Vespa P., Van Horn J., Niethammer M.

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted.

This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image de- composition: (1) normal tissue appearance is captured by a statistical appearance model (via principal component analysis (PCA)), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space.

We demonstrate the effectiveness of our approach on four datasets: the publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing imaging with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Hence, our approach is an effective method for brain extraction for a wide variety of images with high-quality brain extraction results.