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‘MASI lab’

MASI Lab to host multi-year MRI white matter challenge

Feb. 1, 2019—Submissions are due March 7 for a new global challenge hosted by the Medical-imaging Analysis and Statistical Interpretation Lab that focuses on a complex issue in brain mapping – microstructure validation using diffusion MRI. Diffusion MRI, which is non-invasive, has emerged as a key modality for studying normal and abnormal brain development, aging, and brain...

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VISE affiliate awarded 2019 AHA Predoctoral Fellowship

Jan. 16, 2019—A biomedical engineering graduate student who is participating in the Medical Scientist Training Program has received a two-year fellowship aimed at improving global cardiovascular health. Camilo Bermudez Noguera, a VISE affiliate in the MASI Lab, received the 2019 Predoctoral Fellowship from the American Heart Association. “I am incredibly excited for this opportunity. I believe this...

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Yuankai Huo, MASI Lab quoted in Nvidia article “Making Ultrasound Ultra-Speedy with Deep Learning”

Jun. 19, 2018—Abdominal ultrasound tests for organ abnormalities haven’t changed much in the past decade, with a doctor moving a wand over the patient’s abdomen to gaze at blurry images. But the process could get accelerated by a thousand times with improved accuracy, based on deep learning work by U.S. researchers.

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SPIE 2018 Special Workshop organized by VISE steering committee member, Michael Miga

Mar. 6, 2018—Last year, when SPIE put out a Special Issue Call for Papers in SPIE’s Journal of Medical Imaging, Harvie Branscomb Professor of Biomedical Engineering Michael I. Miga, PhD, proposed the first issue ever devoted specifically to image-guided procedures, robotic interventions and modeling. Not only did SPIE welcome the idea, but it has become the largest...

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Highlighting SPIE 2018 student presentations – Cam Bermudez

Mar. 2, 2018—Cam Bermudez, an MD/PhD student in the Medical-image Analysis and Statistical Interpretation (MASI) Lab, attended SPIE Medical Imaging meeting once before but 2018 was a different experience entirely. Bermudez went as a speaker. As lead author, Bermudez presented the paper titled, “Learning implicit brain MRI manifolds with deep learning.” The lab, part of the Vanderbilt...

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MASI Lab teams up with EnvoyAI to develop segmentation algorithms

Jan. 9, 2018—The MASI lab is collaborating on the development of deep learning algorithms for abdomen segmentation that leverage artificial intelligence to better understand and diagnose disease. The MASI lab, affiliated with the Vanderbilt Institute for Surgery and Engineering, is working with a team from EnvoyAI, which aims to simplify access to new AI algorithms by providing...

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VISE 2017 Summer Undergraduate Program concludes with student-led seminar

Sep. 18, 2017—Eight students worked side-by-side graduate students and faculty on active research projects as part of the Vanderbilt Institute for Surgery and Engineering’s 2017 Summer Undergraduate program. Students in biomedical engineering, mechanical engineering, electrical engineering, and computer science spent ten weeks in VISE labs under the supervision of a VISE affiliate faculty member. The paid program...

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VISE affiliate Bennett Landman, Ph.D., uses big data to solve big medical problems in the MASI lab

Feb. 14, 2017—As an initial member of the Vanderbilt Institute on Surgery and Engineering, the Medical-image Analysis and Statistical Interpretation (MASI) lab seeks to transform medical imaging from pixels to information to improve patient care. We lead Personalized Medicine with Medical Imaging Informatics (PM2I2) efforts in translational research to explore innovative, clinically useful techniques. Our primary technical...

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