Perelman School of Medicine at the University of Pennsylvania

Section for Biomedical Image Analysis (SBIA)

participating with CBICA

Confetti: Connectivity-based Fiber Extraction and Identification

Statistical analyses over white matter tracts can contribute greatly towards understanding many mechanisms of the brain since tracts are representative of the connectivity pathways. The main challenge with tract-based studies is the extraction of the tracts of interest in a consistent and comparable manner over a large group of individuals without drawing the inclusion and exclusion regions of interest (ROI). In this project, we develop a framework for automated extraction of white matter tracts. Representation of individual fibers uses connectivity signatures of fibers to establish an easy correspondence between different subjects, as well as providing a representation that is robust to edema, mass effect, and tract infiltration. The framework includes a fiber bundle atlas that emulates the expert knowledge on the tracts, using white matter fibers of healthy individuals. The white matter tracts of a new individual are then extracted based on the definitions encoded in the atlas. This provides an automated tract identification paradigm that corrects for artifacts created by tumor edema and infiltration, as well as providing a consistent, accurate method that is invariant to ROI selection biases.


1. B. Tunç, M. Ingalhalikar, D. Parker, J. Lecoeur, R. L. Wolf, L. Macyszyn, S. Brem, R. Verma, Individualized Map of White Matter Pathways: Connectivity-based Paradigm for Neurosurgical Planning, Neurosurgery, Vol. 79 (4), pp. 568-77, 2016.

2. B. Tunç, W. A. Parker, M. Ingalhalikar, R. Verma, Automated tract extraction via atlas based Adaptive Clustering, NeuroImage, Vol. 102 (2), pp. 596-607, 2014.

3. B. Tunç, A. R. Smith, D. Wasserman, X. Pennec, W. M. Wells, R. Verma, K. M. Pohl, Multinomial Probabilistic Fiber Representation for Connectivity Driven Clustering, Information Processing in Medical Imaging (IPMI), 2013.