Welcome to the Deep Learning for Precision Health Lab

We develop the theory and application of deep learning to improve healthcare.

Overview of our projects and impact on healthcare

We develop the theory and application of deep learning to improve diagnoses, prognoses and therapy decision making. We move the boundaries of what predictive models can achieve by developing new methods and tools for machine learning and deep learning and improve their applicability and performance on information rich, biomedical problems. Our applications of machine learning focus on building predictive models for neurodegenerative diseases, neurodevelopmental disorders and mental disorders. To achieve these aims, we employ the latest, most advanced, non-invasive neuroimaging acquisitions techniques and develop optimized post-processing for: multi-contrast MRI, EEG/MEG, PET/SPECT.

Advancing the Theory of Deep Learning


How can deep learning models be optimally tailored to each new problem to maximize prediction performance? How can domain expertise from clinicians be embedded into deep learning models? How can causal information be extracted in longitudinal deep learning data analyses to avoid reliance on purely correlation based information? We are tackling these problems with a combination of innovative algorithmic development for automated hyperparameter optimization, Bayesian Probability theory, and Information theory. Our focus is on making customized deep learning solutions available to any researcher, including those without machine learning expertise. Our approaches optimize the use of limited labeled training data and provide detailed information about what the models have learned to reveal how predictions are made.

Developing the application of deep learning


How can we improve our currently subjective diagnoses of subtypes brain disorders and diseases that have overlapping symptoms? How can we identify new gene targets for spectrum disorders using the exquisite phenotypes provided by functional neuroimaging? How do we identify pre-treatment biomarkers of therapy response so that optimal patient management decisions can be made before therapy starts? We are addressing these questions by building deep learning predictive models that combine quantitative, multimodal neuroimaging data with multi-omic data and clinical information to reduce uncertainty and improve patient care. This facilitates getting patients the correct treatment sooner, when it can do the most good. Working together with our clinical collaborators, our models are used to cluster disease subtypes and identify the best treatment for each individual patient.

About the lab

The lab is an active part of the Lyda Hill Department of Bioinformatics. We are also affiliated with the Research Division of the Radiology Department and the Advanced Imaging Research Center. We are also affiliated with the Biomedical Engineering Program, the Computational and Systems Biology Track and the Neuroscience Dept.


Albert Montillo, Ph.D.

Principal Investigator

Faculty Page

Son Nam Nguyen, Ph.D.

Postdoctoral Fellow

Son has obtained a Ph.D. in Electrical Engineering with a focus on machine learning from the University of Texas at Arlington. He has developed advanced extensions to the backpropagation algorithms using second order training methods to substantially improve their convergence speed. He has adapted and demonstrated his approaches for both feed forward neural networks and for convolutional neural networks. His expertise includes Tensorflow, PyTorch, Matlab, scientific programing and cloud computing. His postdoctoral research focuses on additional improvements to machine learning methodology as well as their clinical applications including oncological applications and the quantitative analysis of diagnostic MRI.

Alex Treacher


PhD student

Alex majored in Physics with concentrations in chemistry and mathematics and graduated in 2014 from the University of North Texas. Alex’s undergraduate research focused on quantitative simulations of anti-matter to optimize experimental parameters describing the effect of gravity on anti-matter. Subsequently he developed computational algorithms for the analysis of mass-spectral lipidomics data working closely with Jeff McDonald at UT Southwestern. Since 2017 Alex has been perusing a PhD at UTSW. In Dr. Montillo's lab, Alex’s research focus on the development of methods to automatically optimize deep learning models to inform prognosis and diagnosis in neurodevelopmental and neurodegenerative diseases. Through his work, Alex is generating clinically viable tools to aid in the treatment of Parkinson’s and elucidating deeper understanding of the molecular underpinnings of neurological disease mechanisms.

Aixa Andrade Hernandez

Medical Physics

PhD student

Aixa has a MSc. in Medical Radiation Physics from McGill University and a Bachelor's degree in Physics from the National Autonomous University of Mexico. She is interested in artificial intelligence (AI) as a powerful tool to derive insights from data and excited to explore its potential applications in medicine. She is currently pursuing her Ph.D. degree at UT Southwestern Medical center in Dr. Albert Montillo's lab, where she is developing AI skills and novel tools. She hopes to apply these techniques to discover computationally-driven solutions for human disease. Some of Aixa's hobbies are dancing, painting and reading. She enjoys open-air activities and traveling to different places around the world.

Krishna Kanth Chitta, M.S.

Computational Scientist

Krishna has formal training in medical image analysis, deep learning and computer vision with special emphasis in the physics and clinical applications of Magnetic Resonance Imaging. He has experience in methods for detecting and quantitating structures/lesions in MRI datasets, Fluorescent microscopy images and colonoscopy. He is developing machine learning algorithms including advanced convolutional neural networks to solve image analysis challenges involving multi-modal medical imaging.

James Yu



James obtained his MD at the Case Western Reserve University's School of Medicine and a Bachelor's degree in Biochemistry at the University of California, Riverside. While in Medical School, he pursued a MS in Computer Science at Johns Hopkins University with a focus on artificial intelligence and developed an interest in intelligent automation and clinical decision support tools. This has led him to UT Southwestern’s Radiology Program’s Clinician Scientist Track for residency and Dr. Albert Montillo's lab. His research goals involve using state-of-the-art ML approaches to help automate reads of routine imaging studies and to build CDS and segmentation models that can be used in clinical practice.

Atef Ali

Undergraduate Research Assistant

Atef is studying Mathematics, Computer Science, and Statistics as an undergraduate at the University of Minnesota and is working in Dr. Montillo’s lab as part of a year long internship program at UTSW. In this undergraduate research experience, Atef is implementing novel machine learning methods for multiple-label segmentation algorithms for multi-dimensional MRI. After his Bachelor’s, Atef hopes aims to pursue a PhD in Bioinformatics or Machine Learning. In his free time, Atef enjoys biking (when the Minnesota weather permits!), reading, and watching basketball.

Meet the PI

Our PI, 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. He is also an Adjunct Professor at UT Dallas in the School of Engineering in Computer Science and in Biomedical Engineering.

Dr. Montillo received bachelor of science and master of science degrees in Computer Science from RPI and minor concentrations in Electrical Engineering and Cognitive science/Psychology. He obtained his PhD in Computer Science from the University of Pennsylvania where he studied automated image analysis of 4D cardiac MRI and neuroimage co-registration and parcellation (automated quantitative neuroanatomical structure volumetry) with applications to Alzheimer’s. During his studies, Dr. Montillo developed a core neuroanatomical structure labeling algorithm which has been adopted into FreeSurfer, while at Harvard/MIT Martinos Center for Biomedical Imaging, and is now used worldwide. A variant of the algorithm has received FDA approval the first brain parcellation algorithm to do so. After his studies, Dr. Montillo developed a deep learning approach for the decision forest, known as entanglement, which improves prediction accuracy and increases prediction speed while a researcher at the Machine Intelligence and Perception group of Microsoft Research in Cambridge, United Kingdom. Subsequently he joined as a Lead Scientist at General Electric Research Center in upstate New York where he led the development of machine learning based methods for analyzing high volume neuroimaging data. His efforts led to automated methods for brain parcellation (patented), brain lesion quantification, and automated brain-connectivity based prognoses for mild traumatic brain injury (mTBI) – all using advanced multi-contrast MRI. His efforts also led to machine learning algorithms that rank features in imaging genomics studies of Alzheimer’s for automated individualized disease progression prediction. His efforts led to fully automated, machine learning based identification of the content of a imaging scan which prepares a wide range of clinical image data for automated analyses and enables radiation dosage reduction in computed tomography via scout-scans.


** = Corresponding author

Kooner KS, Angirekula A, Treacher AH, Al-Humimat G, Marzban, MF, Chen A, Pradhan R, Tunga N, Wang C, Ahuja P, Zuberi H, Montillo AA. Glaucoma Diagnosis Through the Integration of Optical Coherence Tomography/Angiography and Machine Learning Diagnostic Models. Clinical ophthalmology (Auckland, N.Z.). 2022 August 18. 16, 2685-2697.
[doi: 10.2147/OPTH.S367722 | PMID: 36003072 | PMCID: PMC9394657 ]

Berto S*, Treacher AH*, Caglayan E*, Luo D, Haney JR, Gandal MJ, Geschwind DH, Montillo AA**, Konopka G**. Association between resting-state functional brain connectivity and gene expression is altered in autism spectrum disorder. Nature Communications. 2022 June 9; Vol 13(1):3328.
[ bib | doi: 10.1038/s41467-022-31053-5 | PMID: 35680911 | PMCID: 9184501 ]

Kalecky, K, German, D, Montillo, A, Bottiglieri, R**. Targeted Metabolomic Analysis in Alzheimer’s Disease Plasma and Brain Tissue in Non Hispanic Whites. Journal of Alzheimer's Disease. 2022 Feb 28.
[ Epub | PMID 35253754 | doi: 10.3233/JAD-215448 ]

Raval, V, Nguyen, KP, Pinho, M, Dewey, RB, Trivedi, M, Montillo, AA**. Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI. Neuroinformatics. 2022.
[ pdf | bib | PMID 35291020 | doi: 10.1007/s12021-022-09565-8 ]

Mellema, CJ, Nguyen, KP, Treacher, A, Montillo, A**. Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning. Scientific Reports. 2022.
[ pdf | bib | doi: 10.1038/s41598-022-06459-2 | PMID: 35197468 | PMCID: PMC8866395 | ISBI 2019 slides | ISBI 2020 presentation | code ]

Treacher A, Garg P, Davenport E, Godwin R, Proskovec A, Bezerra LG, Murugesan G, Wagner B, Whitlow CT, Stitzel JD , Maldjian JA, Montillo A**. MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks. NeuroImage, 2021; Vol 241, pp. 118402.
[ bib | doi: 10.1016/j.neuroimage.2021.118402 | PMID: 33730626 | pdf | code ]

Nguyen KP, Fatt CC, Treacher A, Mellema C, Cooper C, Jha MK, Kurian B, Fava M, McGrath PJ, Weissman M, Phillipes ML, Trivedi MH, Montillo A**. Patterns of Pre-Treatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results from the EMBARC Randomized Clinical Trial. Biological Psychiatry. 2022 Mar 15;91(6):550-560.
[ bib | doi: 10.1016/j.biopsych.2021.09.011 | PMID: 34916068 | pdf_preprint | link | supplement | PressRelease ]

Nguyen KP, Raval V, Treacher A, Mellema C, Yu FF, Pinho MC, Subramaniam RM, Dewey RB, Montillo A**. Predicting Parkinson's disease trajectory using clinical and neuroimaging baseline measures. Parkinsonism and Related Disorders. 2021 April; Vol 85, pp. 44-51.
[ bib | doi: 10.1016/j.parkreldis.2021.02.026 | PMID: 33730626 ]

Nguyen S, Polat D, Karbasi P, Moser D, Wang L, Hulsey K, Cobanoglu M, Dogan B, Montillo A**. Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN. MICCAI: International Conference on Medical Image Computing and Computer-Assisted Intervention. 2020 September; Vol 2, pp. 326-334.
[ pdf | bib | doi: 10.1007/978-3-030-59713-9_32 | PMID: 33768221 | PMC7990260 | slides | talk ]

Mellema CJ, Treacher A, Nguyen KP, Montillo A**. Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder. International Symposium on Biomedical Imaging (ISBI). 2020 April; pp. 1022-1025.
[ pdf | bib | doi: 10.1109/ISBI45749.2020.9098555 | PMC7990265 | slides | talk ]

Raval V, Nguyen KP, Gerald A, Dewey RB, Montillo A**. Improved Motion Correction for Functional MRI using an Omnibus Regression Model. International Symposium on Biomedical Imaging (ISBI). 2020 April; 1044-1047.
[ pdf | bib | doi: 10.1109/ISBI45749.2020.9098688 | PMC7990252 | slides | talk ]

Raval V, Nguyen KP, Gerald A, Dewey RB, Montillo A**. Prediction of Individual Progression Rate in Parkinson's Disease Using Clinical Measures and Biomechanical Measures of Gait and Postural Instability. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). 2020 May; 1319-1323.
[ pdf | bib | doi: 10.1109/ICASSP40776.2020.9054666 | PMC7944712 | slides | talk ]

Nguyen KP, Chin Fatt C, Treacher A, Mellema C, Trivedi MH, Montillo A**. Anatomically-Informed Data Augmentation for Functional MRI with Applications to Deep Learning. SPIE Medical Imaging. 2020 February; 113130T.
[ pdf | bib | doi: 10.1117/12.2548630 | PMC7990266 | slides | talk ]

Nguyen KP, Chin Fatt C, Treacher A, Mellema C, Trivedi MH, Montillo A**. Predicting Response to the Antidepressant Bupropion using Pretreatment fMRI. Medical Image Computing and Computer-Assisted Intervention: PRIME. 2019 October;
[ pdf | bib | doi: 10.1007/978-3-030-32281-6_6 | PMID: 31709423 | PMC6839715 | talk ]

Nguyen KP, Fatt CC, Mellema C, Trivedi MH, Montillo A**. Sensitivity of Derived Clinical Biomarkers to rs-fMRI Preprocessing Software Versions. IEEE International Symposium on Biomedical Imaging. 2019 April; 1:1581-1584.
[ pdf | bib | doi: 10.1109/ISBI.2019.8759526 | PMID: 31741703 | PMC6860361 ]

Treacher A, Beauchamp D, Quadri B, Vij A, Fetzer D, Yokoo T, Montillo A**. Deep learning convolutional neural networks for the estimation of liver fibrosis severity from ultrasound texture. Medical Imaging. Computer-Aided Diagnosis (SPIE). 2019 February; 1:109503E1-8.
[ pdf | bib | doi: 10.1117/12.2512592 | PMID: 31741550 | PMC6859455 ]

Mellema C, Treacher A, Nguyen KP, Montillo A**. Multiple Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI. IEEE International Symposium on Biomedical Imaging. 2019; 1:1891-1895.
[ pdf | bib | doi: 10.1109/ISBI.2019.8759193 | PMID: 31741704 | PMC6859452 ]

Dash D, Ferrari P, Malik S, Montillo A, Maldjian J, Wang J**. Determining the Optimal Number of MEG Trials: A Machine Learning and Speech Decoding Perspective. Brain Informatics. 2018 December; 1:163-172.
[pdf | bib | doi: 10.1007/978-3-030-05587-5_16 | doi: 10.1109/ISBI.2019.8759193 | PMID: 31768504 | PMC6876632 ]

Murugesan GK, Saghafi B, Davenport EM, Wagner BC, Urban-Hobson J, Kelley M, Jones D, Powers A, Whitlow C, Stitzel JD, Maldjian JA, Montillo A**. Single Season Changes in Resting State Network Power and the Connectivity between Regions Distinguish Head Impact Exposure Level in High School and Youth Football Players. Medical Imaging: Computer-Aided Diagnosis (SPIE). 2018 February; 1:105750F1-8.
[pdf | bib | doi: 10.1117/12.2293199 | PMID: 31787799 | PMC6884358 ]

Saghafi B, Murugesan G, Davenport E, Wagner B, Urban J, Kelley M, Jones D, Powers A, Whitlow C, Stitzel J, Maldjian J, Montillo A**. Quantifying the Association between White Matter Integrity Changes and Subconcussive Head Impact Exposure from a Single Season of Youth and High School Football using 3D Convolutional Neural Networks. Medical Imaging: Computer-Aided Diagnosis (SPIE). 2018 February; :105750E1-8.
[pdf | bib | doi: 10.1117/12.2293023 | PMID: 31741549 | PMC6859447 ]

O'Neill TJ, Davenport EM, Murugesan G, Montillo A, Maldjian JA**. Applications of Resting State Functional MR Imaging to Traumatic Brain Injury. Neuroimaging Clin N Am. 2017 Nov;27(4):685-696.
[pdf | bib | doi: 10.1016/j.nic.2017.06.006 | PMID: 28985937 | PMC5708891 ]

Famili A, Murugesan G, Wagner B, Smith SC, Xu J, Divers J, Freedman B, Maldjian JA, Montillo A**. Impact of Glycemic Control and Cardiovascular Disease Measures on Hippocampal Functional Connectivity in African Americans with Type 2 Diabetes: a resting state fMRI Study. Radiological Society of North America; 2017 November; c2017
[ pdf | bib ]

Garg P, Davenport EM, Murugesan G, Whitlow C, Maldjian J, Montillo A**. Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography. Medical Image Computing and Computer-Assisted Intervention; 2017 September; 3:374-381.
[pdf | bib | doi: 10.1007/978-3-319-66179-7_43 | PMID: 31656959 | PMC6814159 ]

Saghafi B, Garg P, Wagner B, Smith C, Xu J, Divers J, Madhuranthakam A, Freedman B, Maldjian J, Montillo A**. Quantifying the Impact of Type 2 Diabetes on Brain Perfusion using Deep Neural Networks. Medical Image Computing and Computer Assisted Intervention. 2017 September.
[pdf | bib | doi: 10.1007/978-3-319-67558-9_18 | PMID: 31650132 | PMC6812498 ]

Murugesan G, Garg P, O'Neil T, Wagner B, Whitlow C, Maldjian J, Montillo A**. Automatic Labeling of Resting State fMRI Networks using 3D Convolutional Neural Networks. Pattern Recognition in Neuroimaging. 2017 June.
[ bib]

Garg P, Davenport E, Murugesan G, Wagner B, Whitlow C, Maldjian J, Montillo A**. Automatic Multiple MEG Artifact Detection using 1-D Convolutional Neural Networks without Electrooculography or Electrocardiography. Pattern Recognition in Neuroimaging. 2017 June; 1:1-4.
[pdf | bib | doi: 10.1109/PRNI.2017.7981506 | PMID: 31656826 | PMC6814172 ]

Murugesan G, Famili A, Davenport E, Wagner B, Urban J, Kelley M, Jones D, Whitlow C, Stitzel J, Maldjian J, Montillo A**. Changes in resting state MRI networks from a single season of football distinguishes controls, low, and high head impact exposure. IEEE International Symposium on Biomedical Imaging. 2017 May; 1:464-467.
[pdf | bib | doi: 10.1109/ISBI.2017.7950561 | PMID: 31741701 | PMC6859454 ]

Li B, She H, Keupp J, Dimitrov I, Montillo A, Madhuranthakam A, Lenkinski R, Vinogradov E**. Image registration with structuralized Mutual Information: application to CEST. International Society of Magnetic Resonance In Medicine; 2017 April 22; c2017.
[pdf | bib ]

Famili A, Krishnan G, Davenport E, Germi J, Wagner B, Lega B, Montillo A**. Automatic identification of successful memory encoding in stereo-EEG of refractory, mesial temporal lobe epilepsy. IEEE International Symposium on Biomedical Imaging. 2017; 1:587-590.
[pdf | bib | doi: 10.1109/ISBI.2017.7950589 | PMID: 31741702 | PMC6859446 ]

Muller H, Kelm BM, Arbel T, Cai WT, Cardoso MJ, Langs G, Menze B, Metaxas D, Montillo A, Wells III WM, Zhang S, Chung AC, Jenkinson M, Ribbens A, editors. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. London: Springer book publishers; 2016. 222p.
[doi: 10.1007/978-3-319-61188-4 ]

Menze B, Langs G, Montillo A, Kelm BM, Muller H, Zhang S, Cai W, Metaxas D, editors. Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2015. Switzerland: Springer book publishers; 2016. 182p.
[bib | doi: 10.1007/978-3-319-42016-5 ]

Liu X, Montillo A, inventors. Systems and methods for image segmentation using a deformable atlas. U.S. issued patent #9208572. 2015 December.
[pdf ]

Yin Z, Yao Y, Montillo A, Wu M, Edic PM, Kalra M, De Man B**. Acquisition, preprocessing, and reconstruction of ultralow dose volumetric CT scout for organ-based CT scan planning. Med Phys. 2015 May;42(5):2730-9.
[pdf | bib | doi: 10.1118/1.4921065 | PMID: 25979071 ]

Montillo A**, Song Q, Das B, Yin Z. Hierarchical pictorial structures for simultaneously localizing multiple organs in volumetric pre-scan CT. Medical Imaging: Image processing (SPIE). 2015 March; 1:94130T1-6.
[pdf | bib | doi: 10.1117/12.2082183 | PMID: 31798201 | PMC6886528 ]

Montillo A**, Sharma S, Prastawa P. Feature Selection and Imaging-Genetics Predictions Using a Sparse, Extremely Randomized Forest Regressor. 2014 September. Medical Image Computing and Computer-Assisted Intervention: Workshop on Imaging Genetics, MIT, Boston.
[pdf | bib ]

Menze B, Langs G, Montillo A, Kelm M, Muller H, Zhang S, editors. Medical Computer Vision: Algorithms for Big Data: International Workshop, MCV 2014. Switzerland: Springer; 2014. 211p.
[doi: 10.1007/978-3-319-13972-2 ]

Yin Z, Yao Y, Montillo A, Edic PM, Man BD**. Feasibility study on ultra-low dose 3D scout of organ based CT scan planning. International Conference on Image Formation in X-Ray Computed Tomography. ISBN: 9781510857131. 2014; 1:52-55.
[pdf | PMID: 31788673 | PMC6885018 ]

Yan Z, Zhang S, Liu X, Metaxas D, Montillo A**. Accurate whole-brain segmentation for Alzheimer’s disease combining an adaptive statistical atlas and multi-atlas. Medical Image Computing and Computer-Assisted Intervention. 2013 September;
[pdf | bib | doi: 10.1007/978-3-319-05530-5_7 | PMID: 31723945 | PMC6853627 ]

Montillo A**, Song Q, Bhagalia R, Srikrishnan V. Organ localization using joint AP/LAT view landmark consensus detection and hierarchical active appearance models. Medical Image Computing and Computer-Assisted Intervention. 2013 September; 3:138-147.
[pdf | bib | doi: 10.1007/978-3-319-05530-5_14 | PMID: 31915754 | PMC6947663 ]

Bianchi A, Miller JV, Tan ET, Montillo A**. Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests. Proc IEEE Int Symp Biomed Imaging. 2013 Apr;2013:748-751.
[pdf | bib | doi: 10.1109/ISBI.2013.6556583 | PMID: 25404996 | PMC4232942 ]

Montillo A**, Song Q, Liu X, Miller JV. Parsing radiographs by integrating landmark set detection and multi-object active appearance models. Medical Imaging : Image processing (SPIE). 2013 Mar 13;8669:86690H.
[pdf | bib | doi: 10.1117/12.2007138 | PMID: 25075265 | PMC4112100 ]

Liu X**, Montillo A, Tan E, Schenck J. iSTAPLE: improved label fusion for segmentation by combining STAPLE with image intensity. Medical Imaging : Image processing (SPIE). 2013 March; 1:86692O1-6.
[pdf | bib | doi: 10.1117/12.2006447 | PMID: 31741552 | PMC6859448 ]

Liu X**, Montillo A, Tan ET, Schenck JF, Mendonca P. Deformable atlas for multi-structure segmentation. Med Image Comput Comput Assist Interv. 2013;16(Pt 1):743-50.
[pdf | bib | doi: 10.1007/978-3-642-40811-3_93 | PMID: 24505734 ]

Yan Z, Zhang S, Liu X, Metaxas DN, Montillo A**. Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer's disease. IEEE International Symposium on Biomedical Imaging. 2013; 2:1202-1205.
[pdf | bib | doi: 10.1109/ISBI.2013.6556696 | PMID: 31788155 | PMC6884356 ]

Montillo A**, Tu J, Shotton J, Winn J, Iglesias JE, Metaxas DN, Criminisi A. Entangled Forests and Differentiable Information Gain Maximization. Decision Forests for Computer Vision and Medical Image Analysis. London: Springer; 2013. Chapter 19; p.273-293.
[pdf | bib | doi: 10.1007/978-1-4471-4929-3_19 ]

Menze B, Langs G, Lu L, Montillo A, Tu Z, Criminisi A, editors. Medical Computer Vision. Large Data in Medical Imaging. Berlin: Springer-Verlag Berlin Heidelberg; 2013. 292p.
[ doi: 10.1007/978-3-319-05530-5 ]

Montillo A**. Context Selective Decision Forests and their application to Lung Segmentation in CT Images. Medical Image Computing and Computer-Assisted Intervention: PIA. 2011 September; 1:201-212.
[pdf | bib | ISBN: 978-1-4662-0016-6 | URL ]

Montillo A**, Shotton J, Winn J, Iglesias JE, Metaxas D, Criminisi A. Entangled decision forests and their application for semantic segmentation of CT images. Inf Process Med Imaging. 2011;22:184-96.
[pdf | bib | PMID: 21761656 ]

Iglesias JE, Konukoglu E, Montillo A, Tu Z, Criminisi A**. Combining generative and discriminative models for semantic segmentation of CT scans via active learning. IPMI 2011, LNCS 6801, pp. 25–36, 2011.
[pdf | bib | PMID: 21761643 ]

Montillo A**, Metaxas DN, Axel L. Incompressible biventricular model construction and heart segmentation of 4D tagged MRI: application to right ventricular hypertrophy. Medical Image Computing and Computer-Assisted Intervention: CBM. 2010 October; 1:143-155.
[pdf | bib | doi: 10.1007/978-1-4419-9619-0_15 | PMID: 31742255 | PMC6860908 ]

Montillo A**, Ling H. Age regression from faces using random forests. IEEE International Conference on Image Processing. IEEE International Conference on Image Processing. 2010 February; 1:1-4.
[pdf | bib | doi: 10.1109/ICIP.2009.5414103 | PMID: 31772508 | PMC6879191 ]

Axel L**, Montillo A, Kim D. Tagged magnetic resonance imaging of the heart: a survey. Med Image Anal. 2005 Aug;9(4):376-93.
[ pdf | bib | doi: 10.1016/j.media.2005.01.003 | PMID: 15878302 ]

Gopalakrishnan V, Montillo A, Bachelder I, inventors. Methods and apparatus for determining the orientation of an object in an image. U.S. issued patent #6898333. 2005 May.

Park K, Montillo A, Metaxas DN, Axel L**. Volumetric Heart Modeling and Analysis. Communications of the ACM. 2005 February; 48(2):43-48.
[pdf | bib | doi: 10.1145/1042091.1042118 | PMID: 31662583 | PMC6818726 ]

Gopalakrishnan V, Montillo A, Bachelder I, inventors. Methods and apparatuses for generating a model of an object from an image of the object. U.S. issued patent #6813377. 2004 November.

Manglik T, Axel L, Pai VM, Kim D, Dugal P, Montillo A, Zhen Q**. Use of Bandpass Gabor Filters for Enhancing Blood-Myocardium Contrast and Filling-in tags in tagged MR Images. International Society of Magnetic Resonance In Medicine; 2004 May; c2004.
[pdf ]

Montillo A**, Metaxas D, Axel L. Extracting tissue deformation using Gabor filter banks. Medical Imaging: Physiology, Function, and Structure from Medical Images (SPIE). 2004 April; 1:1-9.
[pdf | bib | doi: 10.1117/12.536860 | PMID: 31824125 | PMC6902438 ]

Montillo A, Bachelder I, inventors**. Methods and apparatuses for identifying regions of similar texture in an image. U.S. issued patent #6647132. 2003 November.

Montillo A**, Metaxas DN, Axel L. Automated deformable model-based segmentation of the left and right ventricles in tagged cardiac MRI. Medical Image Computing and Computer-Assisted Intervention. 2003 October; 1:507-515.
[pdf | bib | doi: 10.1007/978-3-540-39899-8_63 | PMID: 31663082 | PMC6818716 ]

Qian Z, Montillo A, Metaxas D, Axel L**. Segmenting cardiac MRI tagging lines using Gabor filter banks. IEEE Engineering in Medicine and Biology Society. 2003 September; 1:630-633.
[pdf | bib | doi: 10.1109/IEMBS.2003.1279834 ]

Montillo A**, Axel L, Metaxas D. Automated Correction of Background Intensity Variation and Image Scale Standardization in 4D Cardiac SPAMM-MRI. International Society of Magnetic Resonance In Medicine; 2003 July; c2003.
[pdf | bib ]

Montillo A**, Udupa J, Axel L, Metaxas D. Interaction between noise suppression and inhomogeneity correction in MRI. Medical Imaging: Image Processing (SPIE). 2003 May; 1:1-12.
[pdf | bib | doi: 10.1117/12.483555 | PMID: 31745377 | PMC6863362 ]

Montillo A, Bachelder I, Marrion CC, inventors. Methods and apparatuses for measuring an extent of a group of objects within an image. U.S. issued patent #6571006. 2003 February.

Montillo A, Bachelder I, Marrion CC, inventors. Methods and apparatuses for refining a geometric description of an object having a plurality of extensions. U.S. issued patent #6526165. 2003 February.

Montillo A**, Metaxas DN, Axel L. Automated segmentation of the left and right ventricles in 4D cardiac SPAMM images. Medical Image Computing and Computer-Assisted Intervention. 2002 September; 1:620-633.
[pdf | bib | doi: 10.1007/3-540-45786-0_77 | PMID: 31737869 | PMC6857637 ]

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**. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002 Jan 31;33(3):341-55.
[pdf | bib | doi: 10.1016/s0896-6273(02)00569-x | PMID: 11832223 ]

Copyright Notice: The materials are presented here may only be used for research purposes. Copyright and all rights therein are retained by authors or by other copyright holders. In most cases, these works may not be reposted or distributed without the explicit permission of the copyright holder.

Code Repositories

We are pleased to support and contribute to the open source research and development community. Codes and other resources for our publications and research efforts can be found on github through the following link: https://github.com/DeepLearningForPrecisionHealthLab?tab=repositories

Positions Available

The laboratory of Albert Montillo in the Bioinformatics Department at the UT Southwestern Medical Center is an interactive and collaborative team conducting cutting-edge research to advance the theory and application of machine learning for medical image analysis. We address unmet clinical needs by forming predictive models that make diagnoses and prognoses more precise and advance neuroscience by furthering the understanding of mechanisms in disease and intervention. Medical image analysis software the lab has developed include machine learning-based methods for labeling structures throughout the brain (parcellation), versions of which are used worldwide and FDA approved. The lab has built deep learning methods to label networks in resting state fMRI and detect artifacts in MEG. The lab has pioneered deep learning decision forests that increase prediction accuracy while reducing prediction time and outcome prediction methods using structural and functional connectomics. Building off these capabilities, we plan to develop novel modeling and outcome prediction tools for mental & neurodevelopmental disorders, and neurodegenerative diseases.

The lab is co-located within the Bioinformatics Department on UT Southwestern’s south campus and embedded in the Radiology Department on north campus. We are an integral part of the Advanced Imaging Research Center, and work closely with research groups within Neuroscience, Neurology, Psychiatry, Radiation Oncology, and Surgery. Lab members have access to extensive computational resources, including the >6,800-core cluster with >8 Petabyte of storage available through UTSW’s high-performance infrastructure ( BioHPC ). Members have access to multiple research-dedicated scanners (such as 7T and 3T MRI) and the opportunity to work on a range of image analysis, machine learning and modeling projects on interdisciplinary teams, and participate in all aspects of method development and data analysis with collaborators.

UT Southwestern Medical Center is an Affirmative Action/Equal Opportunity Employer. Women, minorities, veterans and individuals with disabilities are encouraged to apply.

Current and Prospective Ph.D. and M.D/Ph.D. Students

Current students (Ph.D. students at UT Southwestern, UTD, UTA, SMU and MSTP MD/Ph.D. students) are welcome to join our team by emailing me to arrange a meeting. The research experience in our lab provides a great opportunity to supplement your background in computer science, engineering, applied math, physics or neuroscience with robust training in scientific algorithm development and computational modeling while conducting cutting-edge research to advance the theory and application of machine learning for medical image analysis.

Prospective students looking to apply for UTSW graduate school admission must apply by the university’s December 1st deadline, and preferably by November 1st. Be sure to explicitly indicate your interest in my lab in your application. For prospective Ph.D. students, the Ph.D. program in Biomedical Engineering, and in particular the Imaging Track is one often pursued by students in our lab. For M.D./Ph.D. applicants, the MSTP admission deadline is November 1st.

Postdoctoral Researchers

Two postdoctoral positions are available in the Deep Learning for Precision Health lab. Applications are invited for a 2 to 3-year computational postdoctoral research position. The researchers will develop novel deep learning models to predict diagnoses and outcomes from patient data including imaging (fMRI, diffusion MRI, MEG/EEG, PET/SPECT) and corresponding genomic, metabolic and clinical data. Potential projects include theoretical or applied method development. Theoretical projects target the development of 1) improved visualization of network learned abstractions, and 2) streamlined network parameter optimization. Applied projects include advancing the state-of-the-art in methods for: 1) discovering image-based biomarkers, including advanced brain connectivity measures, and differentially expressed metabolic markers and genes for disease diagnosis, and treatment outcome prediction in mental & neurodevelopmental disorders and neurodegenerative diseases. And 2) optimizing non-invasive brain stimulation therapies.

Ideal applicants will have:
- Ph.D. degree in Computer Science, Electrical or Biomedical Engineering, or related field.
- Experience in medical image analysis including familiarity with at least 1 image data type: MRI, PET, CT, MEG/EEG.
- Machine learning experience in one or more of the following: deep learning: neural nets (RNN,CNN,DNN), DCGAN, deep RL, transfer learning, autoencoders; classical or shallow learning methods; probabilistic graphical models; optimization; image recognition, registration & segmentation.
- Strong programming skills including experience with at least 1 ML Python library: Keras, scikit-learn, TensorFlow, PyTorch, Nilearn.
- At least 2 first author papers published and writing skills in English.

To apply, email to Dr. Montillo [Albert.Montillo@UTSouthwestern.edu] and include your CV, names and addresses of three references, statement of research accomplishments and future goals, preferably as one single PDF-document. Use the subject line “PostdocApplicant: ”.

For additional details download Postdoctoral research position (PDF).

Scientific Programmer

The laboratory of Albert Montillo in the Bioinformatics Department of UT Southwestern Medical Center is seeking a full time Scientific Programmer for studies of mental & neurodevelopmental disorders and neurodegenerative diseases. The Scientific Programmer will use multimodal MRI, and MEG/EEG data to study structural and functional circuit changes, and PET/SPECT, CT to study metabolic and pathophysiological changes associated with diagnosis and prognoses. The main responsibilities of the position include: implementing and optimizing image processing, computational and analyses pipelines for large-scale multimodal brain imaging data and corresponding clinical data. The lab is an interactive and collaborative team directed by Albert Montillo, Ph.D., conducting cutting-edge research to advance the theory and application of machine learning for the analysis of medical images. The lab addresses unmet clinical needs by forming predictive models that make diagnoses and prognoses more precise and advance neuroscience by furthering the understanding of mechanisms in disease and intervention. You will work directly with him and an array of principle investigators, collaborators and trainees.

Ideal applicants will have:

- B.A. or B.S. Degree in Computer Science, Electrical Engineering, Biomedical Engineering or a related field with three (3) years scientific software development; Master’s or Ph.D. preferred. Software development experience on high performance compute clusters or GPU-based machine learning is a strong plus. Will consider record of success in publishing computational results in lieu of experience.
- Familiarity with at least 1 image data type: MRI, PET/SPECT, CT, MEG/EEG & format: NIFTI, DICOM.
- Experience in at least 1 neuroimage analysis pipeline: NiPype, SPM, FSL, AFNI, FreeSurfer; for diffusion MRI: Camino, DTI-TK, DiPy, TrackVis, DTI/DSI studio, ExploreDTI; for MEG/EEG: Brainstorm, EEGLAB, FieldTrip, MNE, NUTMEG.
- At least 2 years of experience in Linux, Python and 1 other language (Matlab, R, C/C++).
- Optional but helpful: Practical experience in machine learning, Git, and C++/cMake software development.

To apply, email to Dr. Montillo [Albert.Montillo@UTSouthwestern.edu] and include your CV and names and addresses of three references, preferably as one single PDF-document. Use the subject line “ScientificProgrammer: ”.

For additional details download Scientific programmer position (PDF).


August 2022

Alex's work on machine learning for glaucoma diagnosis publishes in Clinical Ophthalmology. Great job Alex!

Dr. Montillo teaches mathematical modeling with python in the Programming Bootcamp at UTSW along with TAs: Aixa and Alex.


Cooper and Kevin successfully defend their PhD theses. Congratulations!

Summer lab pool party at the Montillo's residence. Fun in the sun celebrating our many successes this year!

May 2022

Dr. Montillo gives invited talk on MEG artifact detection via spatiotemporal deep learning to ASNR conference.

Krishna's paper on brain segmentation via deep learning accepted into this year's OHBM conference.

Dr. Montillo teaches module 2 (Object Oriented Programming) in the Bioinformatics Software Engineering course

April 2022

PhD student, Aixa Andrade Hernandez joins the lab. Welcome Aixa!

Albert teaches new course at UTSW on Architectures and Applications of Deep Learning with a focus on GANs and VAEs.

March 2022

Welcome to rotation students Austin Marckx and Conor McFadden!

February 2022

Kevins's manuscript is featured in multiple press releases at UTSW, Forbes, and Science Daily. Awesome, Kevin!

Karel's manuscript defining metabolites predictive of Alzheimer’s Disease in blood plasma and donated brain tissue was accepted into the Journal of Alzheimer’s Disease. Nice, Karel!

January 2022

Vyom's manuscript on the pitfalls and recommended strategies and metrics to suppress fMRI motion artifacts was accepted into Neuroinformatics. Excellent work, Vyom!

Cooper's manuscript deatiling the reproducible neuroimaging features which enable diagnosis of Autism Spectrum Disorder with machine learning was accepted into Scientific Reports. Great job Cooper! s

November 2021

Alex’s manuscript on automated removal of artifact from magnetoencephalography is published in NeuroImage. Awesome job Alex!

September 2021

Kevin Nguyen’s manuscript on the prediction of Antidepressant outcomes for Major Depressive Disorder patients from fMRI is published in Biological Psychiatry. Well done Kevin!

August 2021

Albert teaches Python Programming Bootcamp to incoming PhD students at UTSW.

March 2021

Congratulations to former undergraduate student Yenho Chen who has been admitted to the Machine Learning Ph.D. program at Georgia Tech with the President’s Fellowship award.

Congratulations to former high school student Meyer Zinn who was admitted to the Computer Science BS program at the University of Texas Austin with the Turing Scholarship award.

February 2021

Kevin Nguyen’s manuscript on Parkinson’s disease prognostics from fMRI is published in Parkinsonism and Related Disorders. Nice job Kevin!

January 2021

Welcome to undergraduate Atef Ali, who has joined the lab as part of the UTSW Bioinformatics Gap Year program! Also welcome to rotation student Mahak Virlley!

September 2020

Son Nguyen gives a talk at MICCAI on predicting breast cancer metastases to the axillary lymph nodes. Well done Son!

August 2020

Group outing: team Montillo takes to the Katy trail for a morning bike ride. Fun!

July 2020

Team Montillo hosts several codefests exchanging best ML implementation practices.

June 2020

Vyom graduates and is accepted into the MD/PhD program at the University of Washington. Great news!

May 2020

Son Nguyen's breast cancer prognostics full-length peer reviewed paper is accepted into premier conference, MICCAI. Awesome!

April 2020

Cooper reveals features important for deep learning model to diagnose Autism at ISBI. Vyom presents an omnibus model for improved motion suppression in fMRI. Excellent work!

March 2020

Vyom presents prediction of individual's rate of Parkinson's progression at ICASSP from biomechanics. Well done.

February 2020

Kevin Nguyen presents first ever data augmentation approach for 4D fMRI which improves prediction performance at SPIE Medical Imaging. Nice job Kevin!

January 2020

Kevin and Albert present methods for Major Depression Disorder treatment response prediction at UTSW.

November 2019

Welcome Postdoctoral Fellow, Son Nam Nguyen!
Albert chairs the session on Artificial Intelligence in Radiology at the annual ASFNR meeting in San Francisco, CA.

September 2019

NIH F31 fellowship awarded to Cooper. Congratulations Cooper!

June 2019

Albert attends ICML and CVPR in Los Angeles

June 2019

Yenho Chen receives Postbaccalaureate Intramural Research Training Award and starts a research scientist position at NIH's Center for Multimodal Neuroimaging within Dr Pereira's Machine Learning Team. Way to go Yenho!

May 2019

Albert gives invited talk on Machine Learning to radiologists at the American Society of Neuroradiology (ASNR) in Boston, MA

April 2019

Multiple F30 and F31 fellowships submitted. Way to go students!!
Cooper presents his research on Autism diagnosis and Kevin’s research on Major Depression Disorder at ISBI in Italy.
Wedding bells for Alex. Congrats Alex!!

March 2019

Albert teaches deep learning in the UTSW Bioinformatics nanocourse, Machine Learning.
It’s a boy! Baby Anthony born to Albert and Andrea. Wohoo!!

February 2019

Alex Treacher presents on Liver Fibrosity diagnosis at SPIE Medical Imaging. Go Alex!

January 2019

Welcome Green Fellow student Vyom Raval!

December 2018

New website goes live! Thank you for visiting!
Albert gives invited conference talk at Brain Informatics conference.

November 2018

Bioinformatics Dept Hackathon 2018 is a super success; congrats Alex for winning an award!

October 2018

Welcome new MSTP graduate student Cooper!
Welcome rotation student Paul!

July 2018

Welcome new MSTP graduate student Kevin!

May 2018

Welcome new graduate student Alex!

April 2018

Albert gives invited talk, Deep learning for artifact detection in MEG, at the 2018 International Workshop on Interactive and Spatial Computing (IWISC).

March 2018

Albert joins program committee of the SPIE Medical Imaging conference.

February 2018

Behrouz and Gowtham deliver oral presentations at SPIE Medical Imaging conference in Houston, TX.
A team of bioinformatics researchers (Drs. Montillo, Rajaram and Cobanoglu) develop and teach a new nanocourse, Machine Learning I, to researchers (grad students, postdocs, faculty) from across the UTSW. Highly positive reviews! Plans underway for subsequent offerings.
Welcome to our new scientific programmer Danni!

January 2018

Albert gives invited talk, Deep learning: a new tool for analyzing Big Neuroimaging Datasets at the jointly (UTD, UTSW) sponsored symposium, Neuroimaging is a team sport.

December 2017

Albert receives appointment in the newly formed Bioinformatics Department at UTSW
Albert trains researchers in neuroimage analysis with SPM.

November 2017

Albert is interviewed for research contributions in machine learning for radiology at RSNA.

September 2017

2 papers presented at the International conference, Medical Image Computing and Computer Assisted Intervention (MICCAI) including Convolutional Neural Networks for artifact detection in MEG, and deep neural networks for quantifying the association between type-2 diabetes management and brain perfusion measured via ASL MRI.

August 2017

2 abstracts accepted to American Society for Functional Neuroradiology (ASFNR). Congratulations Behrouz and Gowtham!

July 2017

2 papers presented at Pattern Recognition for Neuro Imaging (PRNI) on 3D convolutional neural networks for resting state network labeling for rs-fMRI and deep convolutional neural networks for MEG cardiac artifact detection.
1 paper presented at Human Brain Mapping conference on machine learning that uses resting state fMRI to accurately predict head impact exposure in youths playing a single season of football.

May 2017

Albert joins the Research Committee of American Society of Neuroradiology.
Albert teaches Mathematics for Medicine to medical students at UTSW including topics of Bayesian Decision Theory and Deep Learning.

April 2017

Afarin Famili successfully defends master’s thesis using machine learning to detect functional connectivity changes in epilepsy & diabetes. Congrats Afarin!
Albert gives invited conference talk: Machine Learning in functional Neuroimaging at the American Society of Neuroradiology in Los Angeles.
4 papers presented at UTSW Radiology Research Day by Prabhat Garg, Gowtham Murugesan, Afarin Famili!
Gowtham Murugesan presents 2 papers at IEEE International Symposium on Biomedical Imaging (ISBI) in Melbourne, Australia

March 2017

Abstract accepted to Organization for Human Brain Mapping (OHBM) conference

February 2017

Two papers Accepted to IEEE International Symposium on Biomedical Imaging (ISBI) Meeting Conference!
Albert attends annual mtg of American clinical MEG society (ACMEGS).

January 2017

Welcome to our new postdoc, Behrouz and research scientist, Anand to the lab!

December 2016

Welcome aboard new trainee, Gowtham!

Sept 2016

Welcome aboard graduate student, Afarin to the lab!

June 2016

King Foundation grant awarded to Drs. Montillo and Moore.

May 2016

Albert gives invited conference talk at American Society of Neuroradiology (ASNR) in Washington DC: Machine Learning for Neuroimaging.

February 2016

Albert gives seminar talks to Mathematics Dept at UT Dallas and Statistics Dept at Southern Methodist University.

November 2015

Albert attends MEG training at McGill.

Former Lab Members and Trainees

Kevin Nguyen

Biomedical Engineering

MD/PhD student

Cooper Mellema

Biomedical Engineering

MD/PhD student

Vyom Raval, B.S.

UTD/UTSW Greenfellow

Undergraduate Researcher

Prabhat Garg, M.D.


Graduate Researcher

Meyer Zinn

St. Mark's School of Texas

Summer Intern

Yenho Chen, B.S.

UTD/UTSW Greenfellow

Undergraduate Researcher

Behrouz Saghafi, Ph.D.

Postdoctoral Researcher

Anand Kadumberi, M.S.

Senior Research Associate

Mahak Virlley


PhD student

Afarin Famili, B.S.

Graduate Research Assistant

Janis Iourovitski

California Polytechnic

AMGEN scholar

Danni Luo


Scientific Programmer

Get in touch

Deep Learning for Precision Health lab
Lyda Hill Department of Bioinformatics
UT Southwestern Medical Center
5323 Harry Hines Blvd. E-Building, E4.350
Dallas, TX 75390
Ph: 214-645-1726


The Department of Bioinformatics is located in the E building at 5323 Harry Hines Blvd., Dallas, Texas. From Harry Hines Blvd., turn southwest onto Sen. Kay Bailey Hutchison Drive. Take the first right onto a drive that leads to Lot 7, Visitor Parking. See Rebekah Craig during your visit for a parking pass. From Visitor Parking, cross the street to the Donald Seldin Plaza. Walk across the plaza to the right, across the area marked D building, which is underground. At the right side to the E building, take an external stairwell down one level to a garden/koi pond area. Enter through the grey double doors and take the elevator to 4th floor. The Department of Bioinformatics entrance is the glass door at the end of the hallway. Please proceed straight thru to the admin team area and a member of the admin team will escort you to your meeting.

UT Southwestern has full information on parking.

We are accessible by public transportation.

We are an 8 minute walk from the Southwestern Medical District/Parkland Station on the DART green and orange rail lines and 5 min walk from the Medical/Market Station on the Trinity Railway Express.