Check out our video on YouTube.
| We are developing automatic systems to track,
and exploit the morphology of the Intracranial Pressure
Signal to improve the understanding and treatment of various
pathophysiologies related to Traumatic Brain Injuries
Bayesian Tracking of Intracranial Pressure Signal Morphology.
F. Scalzo, S. Asgari, S. Kim, M. Bergsneider, and X. Hu. Artif Intell Med, 2011.
Robust Peak Recognition in Intracranial Pressure Signals.
F. Scalzo, S. Asgari, S. Kim, M. Bergsneider, and X. Hu. BioMed Eng OnLine, 2010.
Regression Analysis for Peak Designation in Pulsatile Pressure Signals.
F. Scalzo, P. Xu, S. Asgari, M. Bergsneider, X. Hu. Med & Biol Eng & Comp, 2009.
Clustering and Analysis of Continuous Intracranial Pressure.
X. Hu, P. Xu, F. Scalzo, P. Vespa, M. Bergsneider. IEEE TBME, 2009.
Computer Vision and Computational Fluid
Dynamics (CFD) can be used to reconstruct 3D models of the
brain vessels and simulate blood flow.
CFD Modeling of Symptomatic
Intracranial Atherosclerosis May Predict Risk of Stroke
Recurrence. X. Leng, F. Scalzo, H. Ip, M. Johnson,
A. Fong, F. Fan, X. Chen, Y. Soo, Z. Miao, L. Liu, E. Feldmann,
T. Leung, D. Liebeskind, K. Wong. Plos One. in press,
Noninvasive fractional flow on
MRA predicts stroke risk of intracranial stenosis in
SONIA/WASID. D. Liebeskind, A. Kosinski, M. Lynn,
F. Scalzo, A. Fong, P. Fariborz. Stroke. 44, 2013.
Computational Hemodynamics in Intracranial Vessels Reconstructed from Biplane Angiograms.
F. Scalzo, Q. Hao, A. Walczak, X. Hu, Y. Hoi, K. Hoffmann, D. Liebeskind. ISVC, 2010.
We develop Machine Learning models to personalize treatment and predict the outcome of stroke patients.
Prediction of Hemorrhagic Transformation in Acute Ischemic Stroke
using Permeability Imaging Features. F. Scalzo, J. Alger, X. Hu,
J. Saver, K. Dani, K. Muir, A. Demchuk, S. Coutts, M. Luby,
S. Warach, D. Liebeskind. Magn Reson Imaging. 1(6):961-9, 2013.
Regional Prediction of Tissue Fate in Acute Ischemic Stroke.
F. Scalzo, Q. Hao, X. Hu, and D. Liebeskind. Annals of Biomedical
Engineering. in press, 2012.
Our research on morphological analysis and
noninvasive ICP is the central technology used in a novel
portable TCD-based ICP estimation device; see
We recently obtained the 1st price at the
CASIS award and
are going to use the International Space Station (ISS) as a controlled
environment for improvement for our portable ultrasound device that
measures ICP. Spaceflight effects on intracranial pressure in
astronauts are well known; thus, the experiment will compare
measurements from astronauts with data from TBI patients with the goal
of refining data measurements.
Noninvasive Intracranial Pressure Assessment based on
Data Mining Approach using Nonlinear Mapping
S. Kim, F. Scalzo, M. Bergsneider,
P. Vespa, N. Martin, and X. Hu. IEEE Trans Biomed
Eng, November, 2010.
During my PhD, I developed a hierarchical probabilistic graphical
model to perform visual inference, which could be seen as an
Lee and Mumford's model.
Adaptive patch features for object class recognition with learned hierarchical models. F. Scalzo, J. Piater. CVPR, Beyond Patches Workshop, 2007.
Task-Driven Learning of Spatial Combinations of Visual Features.
S. Jodogne, F. Scalzo, J. Piater. CVPR, Workshop on Learning, 2005.
Statistical learning of visual feature hierarchies.
F. Scalzo, J. Piater. CVPR, Workshop on Learning, 2005.