Michele Aizenberg

 

m-aizenberg-ansari.jpgProfessor of Neurosurgery
maizenberg@unmc.edu

Dr. Aizenberg is certified by the American Board of Neurological Surgery. Dr. Aizenberg completed neurosurgery training at George Washington University and has completed fellowship training in Neurosurgical Oncology at the Surgical Neurology Branch at NIH and at The University of Texas M.D. Anderson Cancer Center. She  specializes in primary and metastatic tumors of the brain.

Dr. Aizenberg has a special interest in brain function, connectivity, eloquence, and reorganization and how it is impacted by oncologic disease and its treatments. Her research focuses on utilizing advanced imaging and analysis techniques, including deep learning, for surgical planning and clinical assessment of brain tumor patients. Her goal is to pioneer methods of identifying individualized care that maximizes outcomes.

Key Manuscripts:
Lookian, P.P., Chen, E.X., Ehlers, L.D., Ellis, D.G., Juneau, P., Wagoner, J., Aizenberg, M.R. The Association of Fractal Dimension to Vascularity and Clinical Outcomes in Glioblastoma. World Neurosurgery. Online ahead of print. June 2022. DOI: 10.1016/j.wneu.2022.06.073 
 
Lookian, P.P., Chen, E.X., Ehlers, L.D., Ellis, D.G., Juneau, P., Wagoner, J., Aizenberg, M.R. The Association of Fractal Dimension to Vascularity and Clinical Outcomes in Glioblastoma. World Neurosurgery. Revisions submitted. Feb 2022.  
 
Ellis, D., Aizenberg, MR. Structural brain imaging predicts individual-level task activation maps using deep learning. Front. Neuroimaging. April 2022. doi.org/10.3389/fnimg.2022.834883
Ellis D.G., Aizenberg M.R. (2021) Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning Framework. In: Crimi A., Bakas S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science, vol 12659. Springer, Cham. doi10.1007/978-3-030-72087-2_4 
 
Ellis D.G., Aizenberg M.R. (2020) Deep Learning Using Augmentation via Registration: 1st Place Solution to the AutoImplant 2020 Challenge. In: Li J., Egger J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science, vol 12439. Springer, Cham. doi10.1007/978-3-030-64327-0_6

Ellis, D., White, M., Hayasaka, S., Warren, D., Wilson, T., Aizenberg, M.R.; Accuracy analysis of fMRI and MEG activations determined by intraoperative mapping. Neruosurg Focus.  2020 Feb 1; 48(2):E13. doi: 10.3171/2019.11.FOCUS19784