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

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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

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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.



LAB MEMBERS


Albert Montillo, Ph.D.

Principal Investigator

Meet the PI


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.

Kevin Nguyen

Biomedical Engineering

MD/PhD student

Kevin earned his BS in Biomedical Engineering in 2016 at Yale University, where he developed an interest in computer vision, medical image analysis, and machine learning. He is currently pursuing MD and PhD degrees in the Medical Scientist Training Program at UT Southwestern. His research interests include creating automated tools for treatment planning and prognosis in psychiatric and neurological diseases such as depression and Parkinson’s Disease. He also hopes to develop techniques to overcome the "big data" requirement in deep learning, allowing deep learning models to train and perform well in data-limited situations, as is the case in most medical imaging problems. In his future career, Kevin plans to become an academic physician-scientist, an educator, and an advocate for the use of machine learning in medicine.

Jana Windsor, M.S., CCRC

Clinical Research Coordinator

Jana is a Certified Clinical Research Coordinator who has a passion for clinical research and whose mission is to achieve excellence in patient-centered care. Before clinical research was a career option to consider, Jana obtained her Masters in Biology with a concentration in applied statistics. She is a highly capable, detail-oriented, and seasoned professional with 20 years combined clinical research, bench research, lab management, and teaching experience. Jana has extensive research study coordination having served in this capacity at UTSW in the Simmons Comprehensive Cancer Center, Cognition and Memory Disorders research, and in Movement Disorders research. She has successfully managed a variety of clinical research studies from Observational clinical studies to Interventional/ Experimental Phase I through Phase III clinical trials. As a study coordinator, Jana facilitates in patient recruitment, consenting, eligibility, quality data acquisition, and communicating with patients and other research staff. When not coordinating research studies, you might find Jana zip-lining in a rain forest, escaping Escape Rooms, singing in the Praise Band, or double two-stepping around a dance floor. However, she treasures most her time spent with family and friends, especially when this involves playing a mean game of dominoes or Pandemic!

Alex Treacher

Bio-Physics

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.

Cooper Mellema

Biomedical Engineering

MD/PhD student

Cooper triple majored in Physics: Biophysics, Computational Neurobiology, and Biochemistry at the University of Washington. Since 2016, he has been pursuing an MD-PhD combined degree at the University of Texas Southwestern Medical Center. He is pursuing a PhD degree in Biomedical Engineering on the Imaging track. His undergraduate research focused on signal processing and causal analysis of Brain-Computer interfaces. Current research focuses on using a combination of neuroimaging, deep learning, and causal inference to develop diagnostic and prognostic algorithms. The overarching goal is to both develop clinically useful tools as well as identify regions of importance in the pathophysiology of neurodevelopmental and neurodegenerative disease. Specifically, he is interested in prior-informed measures of causal connectivity derived from functional imaging and how these connectivity measures are changed in disease state and with treatment. Outside of the lab, Cooper enjoys cooking, hiking, and has been becoming more involved with Search and Rescue.

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.

Vyom Raval

UTD/UTSW Greenfellow

Undergraduate

Vyom is currently pursuing his bachelor's degree in Neuroscience at the University of Texas at Dallas. His research has focused on creating prognostic models for Parkinson's Disease and developing motion correction algorithms for functional MRI analysis. He has also previously engaged in developmental neurolinguistics research using EEG and on the 3D printing and characterization of liquid crystal elastomer materials. He is interested in using machine learning to confront neurological dysfunctions and to uncover principles of neural function. He aims to pursue an MD-PhD and engage deeply in translational research.


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.


Publications


** = Corresponding author

Nguyen KP, Fatt CC, 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 | PMCID: PMC6839715 | youtube ]

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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMID: 31768504 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | bib | PMID: 31788673 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 | PMCID: 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 ]

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 | PMCID: 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 | PMCID: 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 | PMCID: 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.


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 at Albert.Montillo@UTSouthwestern.edu 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 ( http://www.utsouthwestern.edu/labs/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).

News


December 2019

2 NIH Fellowships submitted.


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


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

Prabhat Garg, M.D.

UTSW

Graduate Researcher

Behrouz Saghafi, Ph.D.

Postdoctoral Researcher

Anand Kadumberi, M.S.

Senior Research Associate

Afarin Famili, B.S.

Graduate Research Assistant

Yenho Chen, B.S.

UTD/UTSW Greenfellow

Undergraduate Researcher

Danni Luo

Bioinformatics

Scientific Programmer

Meyer Zinn

St. Mark's School of Texas

Summer Intern

Jainis Iourovitski

California Polytechnic

AMGEN scholar

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
Email

Albert.Montillo[at]UTSouthwestern[dot]edu

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.