Morphometric analysis is a primary algorithmic tool to discover disease and drug related effects on brain anatomy. Neurological degeneration and disease manifest in subtle and varied changes in brain anatomy that can be non-local in nature and effect amounts of white and gray matter as well as relative positioning and shapes of local brain anatomy. State-of-the-art morphometry methods focus on local matter distribution or on shape variations of apriori selected anatomies but have difficulty in detecting global or regional deterioration of matter; an important effect in many neurodegenerative processes. The proposal team recently developed a morphometric analysis based on unbalanced optimal transport, called UTM, that promises to be capable to discover local and global alteration of matter without the need to apriori select an anatomical region of interest. The goal of this proposal is to develop the UTM technology into a software tool for automated high-throughput screening of large neurological image data sets. A more sensitive automated morphometric analysis tool will help researchers to discover neurological effects related to disease and lead to more efficient screening for drug related effects.
Describing anatomical differences in neurological image data set is a key technology to non-invasively discover the effects of disease processes or drug treatments on brain anatomy. Current morphometric analysis focus on local matter composition and on the shape of a priori defined regions of interest. The goal of this proposal is to extend the capabilities of image based morphometric analysis to be able to discover regionally varying deterioration and alteration of matter without the need for fine-grained segmentations and a priori definitions of regions of interest.
