Dr. Albert Montillo received his Ph.D. in Computer Science with a focus on Medical Image Analysis from the University of Pennsylvania, Philadelphia, PA through training in departments of Computer and Information Science and Radiology. His Bachelor's and Master's degrees are in Computer Science from Rensselaer Polytechnic Institute in New York with minors in Electrical Engineering and Psychology.  At UT Southwestern, his primary appointment is in the department of Radiology where he is an assistant professor. He also holds a secondary faculty appointment at the Advanced Imaging Research Center and is a member of the Graduate School of Biomedical Sciences. Prior to moving to UT Southwestern, Dr. Montillo led neuroimaging research efforts at the General Electric Research Center (2011-2014) in New York and has held research positions at Machine Learning and Perception Laboratory of Microsoft Research in Cambridge, United Kingdom, Rutgers University in New Jersey, and the Harvard/MIT Martinos Center for Biomedical Imaging, Boston, MA.

Through his research, Dr. Montillo develops multivariable, multivariate statistical learning algorithms to make diagnoses and prognoses more quantitative and personalized for clinical neuroscience applications including neuropsychiatric, neurodegenerative, and neurodevelopmental brain disorders, and for oncological applications. The algorithms provide new capabilities that enhance triage criteria, improve patient management, and refine inclusion criteria for clinical trials. These capabilities in turn guide clinical decision making and enable faster development of new treatments.

Researchers in Dr. Montillo’s group focus their efforts on four areas. (1) Discovering the statistical correlations between non-invasive functional connectomics features (from fMRI and MEG/EEG) and disease severity and treatment outcomes in TBI, epilepsy, autism, diabetes, schizophrenia and Alzheimer’s. (2) Uncovering associations between radiological imaging signatures (such as structural connectomics from diffusion MRI) and gene expression and optimizing their combined use in diagnostics. (3) Developing novel machine learning methods (such as deep learning decision forests) for personalized medicine. (4) Developing multi-parametric MRI for improved cancer diagnostics and prognostics.

Dr. Montillo is actively engaged in educational efforts spanning UT Southwestern, UT Dallas, and UT Arlington. He teaches at UT Dallas through an adjunct appointment there, serves on Ph.D. thesis committees at UT Arlington and UT Southwestern and is supervising research for graduate students from UT Arlington.


Rensselaer Polytechnic Institu (1992), Computer Sciences
Graduate School
Rensselaer Polytechnic Institu (1993), Computer Sciences
Graduate School
University of Pennsylvania (2004), Medical Imaging

Research Interest

  • Developing statistical, machine learning approaches that extract radiological imaging (MEG/EEG, functional, diffusion, perfusion, metabolic MRI) and imaging-genomic biomarkers and that engender personalized diagnostics and prognostics in clinical neuroscience and oncological applications.
  • Testing novel candidate therapies with such biomarkers for improved patient care. Developing novel algorithmic methods to stratify patients through retrospective data analyses that guide prospective patient care.


Featured Publications LegendFeatured Publications

Acquisition, preprocessing, and reconstruction of ultralow dose volumetric CT scout for organ-based CT scan planning.
Yin Z, Yao Y, Montillo A, Wu M, Edic PM, Kalra M, De Man B Med Phys 2015 May 42 5 2730-9
Hierarchical Pictorial Structures for Simultaneously Localizing Multiple Organs in Volumetric Pre-Scan CT
Albert Montillo, Qi Song, Bipul Das, Zhye Yin Medical Imaging 2015
Feature Selection and Imaging-Genetics Predictions Using a Sparse, Extremely Randomized Forest Regressor with application to Alzheimer?s disease
Albert Montillo, Shantanu Sharma, Marcel Prastawa Medical Image Computing and Computer-Assisted Intervention 2014
Bianchi A, Miller JV, Tan ET, Montillo A Proc IEEE Int Symp Biomed Imaging 2013 Apr 2013 748-751
Parsing radiographs by integrating landmark set detection and multi-object active appearance models.
Montillo A, Song Q, Liu X, Miller JV Proc SPIE Int Soc Opt Eng 2013 Mar 8669 86690H
Deformable atlas for multi-structure segmentation.
Liu X, Montillo A, Tan ET, Schenck JF, Mendonca P Med Image Comput Comput Assist Interv 2013 16 Pt 1 743-50
Accurate whole-brain segmentation for Alzheimer?s disease combining an adaptive statistical atlas and multi-atlas
Zhennan Yan, Shaoting Zhang, Xiaofeng Liu, Dimitris N. Metaxas, Albert Montillo Medical Image Computing and Computer-Assisted Intervention 2013
Organ localization using joint AP/LAT view landmark consensus detection and hierarchical active appearance models
Albert Montillo, Qi Song, Roshni Bhagalia, and Srikrishnan V Medical Image Computing and Computer-Assisted Intervention 2013
Entangled decision forests and their application for semantic segmentation of CT images.
Montillo A, Shotton J, Winn J, Iglesias JE, Metaxas D, Criminisi A Inf Process Med Imaging 2011 22 184-96
Combining generative and discriminative models for semantic segmentation of CT scans via active learning.
Iglesias JE, Konukoglu E, Montillo A, Tu Z, Criminisi A Inf Process Med Imaging 2011 22 25-36


Featured Books Legend Featured Books

Medical Computer Vision: Algorithms for Big Data, Fourth International MICCAI Workshop

Bjoern H. Menze, Georg Langs, Albert Montillo, Michael Kelm, Henning Muller, Shaoting Zhang, Weidong Cai, Dimitri Metaxas (Ed.) (2015). Springer

Medical Computer Vision: Large Data in Medical Imaging, Third International MICCAI Workshop

Bjoern Menze, Georg Langs, Albert Montillo, Henning Muller, Zhuowen Tu (2014). Springer

Professional Associations/Affiliations

  • Organization for Human Brain Mapping (OHBM) (2012)
  • American Society of Neuroradiology (ASNR) (2011)
  • IEEE (senior member) (2002)
  • Medical Image Computing and Computer assisted intervention (MICCAI) (2002)