Albert Montillo is an Assistant Professor in the Lyda Hill Department of Bioinformatics with secondary appointments in the Department of Radiology, the Advanced Imaging Research Center, and Biomedical Engineering within the Graduate School of Biomedical Sciences. He also directs the research of the Deep Learning for Precision Health lab.
Dr. Albert Montillo's formal training is through a 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. There his research focuses on medical image analysis in MRI including applications in neuroradiology and cardiology. Additional extensive training stems from his experience as a research scientist leading neuroimaging analysis efforts at the General Electric Research Center in New York and research at the Machine Intelligence and Perception Laboratory of Microsoft Research in Cambridge, United Kingdom, as well as machine learning based medical image analysis research at the Harvard-MIT Martinos Center for Biomedical Imaging, Boston, MA and Rutgers University in New Jersey.
Dr. Montillo's research focus is advancing the theory and application of deep learning. He develops de novo machine learning algorithms that increase the accuracy and precision of quantitative, integrative analyses of multi-modal brain images. His approaches combine data-driven training & clinician/scientist expert prior knowledge and have both clinical and neuroscience applications. Clinical applications include automating image interpretation for diagnoses and prognoses (assistive second reader), optimizing non-invasive treatments (e.g. tDCS, TMS), automatic clean-up of data, and automatic feature extraction from images. Basic science applications include understanding brain activity in health and in neurological disorders.
Researchers in Dr. Montillo’s group focus their efforts on four areas. (1) Discovering the significant assoicaitons between non-invasive functional and structural connectomics features (from fMRI, MEG/EEG and diffusion MRI) and disease severity and treatment outcomes in pediatric radiology, TBI, epilepsy, autism, diabetes, schizophrenia and Alzheimer’s. (2) Uncovering associations between radiological imaging signatures (such as anatomical and structural connectomics from MRI) and gene expression and optimizing their combined use in prognostics. (3) Developing novel machine learning methods (such as deep learning neural networks) for personalized medicine. (4) Developing multi-parametric MRI for improved cancer diagnostics and prognostics.
Dr. Montillo is actively engaged in supervising, teaching and training of students spanning UT Southwestern, UT Dallas, and UT Arlington. He teaches Machine Learning at UTSW and at UT Dallas through an adjunct appointment there, serves on Ph.D. thesis committees at UT Arlington and UT Southwestern.
- Rensselaer Polytechnic Institu (1992), Computer Sciences
- Graduate School
- Rensselaer Polytechnic Institu (1993), Computer Sciences
- Graduate School
- University of Pennsylvania (2004), Medical Imaging
- 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.
- 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
- Incompressible biventricular model construction and heart segmentation of 4D tagged MRI: application to right ventricular hypertrophy
- Albert Montillo, Dimitris Metaxas, Leon Axel, Medical Image Computing and Computer-Assisted Intervention 2010
- Tagged magnetic resonance imaging of the heart: a survey.
- Axel L, Montillo A, Kim D Med Image Anal 2005 Aug 9 4 376-93
- An anatomical heart model for segmentation, analysis and classification
- Kyoungju Park, Albert Montillo, Dimitris Metaxas, Leon Axel Communications of the ACM 2005
- Extracting tissue deformation using Gabor filter banks
- Albert Montillo, Leon Axel, Dimitris Metaxas Medical Imaging 2004
- Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.
- Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM Neuron 2002 Jan 33 3 341-55
Entanglement and Differentiable Information Gain Maximization. In Decision Forests for Computer Vision and Medical Image Analysis
Albert Montillo, Jilin Tu, Jamie Shotton, John Winn, J. Eugenio Iglesias, Dimitris Metaxas, and Antonio Criminisi (2013). Springer
Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging, Second International MICCAI Workshop
Bjoern Menze, Georg Langs, Le Lu, Albert Montillo, Zhuowen Tu, Antonio Criminisi (Ed.) (2013). Springer
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
- 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)