Mr. Andrew L. Beers Profile

Andrew L. Beers

Research Assistant at Massachusetts General Hospital

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Publications (5)

Proceedings Article | 10 March 2020 Presentation + Paper

An exploration of uncertainty information for segmentation quality assessment

Katharina Hoebel, Vincent Andrearczyk, Andrew Beers, Jay Patel, Ken Chang, Adrien Depeursinge, Henning Müller, Jayashree Kalpathy-Cramer

Proc. SPIE. 11313, Medical Imaging 2020: Image Processing

KEYWORDS: Data modeling, Visualization, Image segmentation, Lung, Medical imaging, Statistical modeling

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Proceedings Article | 9 March 2018 Paper

Anatomical DCE-MRI phantoms generated from glioma patient data

Andrew Beers, Ken Chang, James Brown, Xia Zhu, Dipanjan Sengupta, Theodore Willke, Elizabeth Gerstner, Bruce Rosen, Jayashree Kalpathy-Cramer

Proc. SPIE. 10573, Medical Imaging 2018: Physics of Medical Imaging

KEYWORDS: Tumor growth modeling, Data modeling, Tumors, Tissues, Magnetic resonance imaging, Scanners, Temporal resolution, Head, Data centers, Motion models

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Proceedings Article | 6 March 2018 Presentation + Paper

Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning

James Brown, J. Peter Campbell, Andrew Beers, Ken Chang, Kyra Donohue, Susan Ostmo, R. V. Paul Chan, Jennifer Dy, Deniz Erdogmus, Stratis Ioannidis, Michael Chiang, Jayashree Kalpathy-Cramer

Proc. SPIE. 10579, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Eye, Ophthalmology, Convolutional neural networks, Visualization, Image segmentation, Machine learning, Data centers, Health informatics

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Proceedings Article | 2 March 2018 Paper

Sequential neural networks for biologically informed glioma segmentation

Andrew Beers, Ken Chang, James Brown, Elizabeth Gerstner, Bruce Rosen, Jayashree Kalpathy-Cramer

Proc. SPIE. 10574, Medical Imaging 2018: Image Processing

KEYWORDS: Biomedical optics, Detection and tracking algorithms, Data modeling, Tumors, Tissues, Magnetic resonance imaging, Image segmentation, Neural networks, Brain, Clinical trials

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Proceedings Article | 13 March 2017 Presentation + Paper

Making sense of large data sets without annotations: analyzing age-related correlations from lung CT scans

Yashin Dicente Cid, Artem Mamonov, Andrew Beers, Armin Thomas, Vassili Kovalev, Jayashree Kalpathy-Cramer, Henning Müller

Proc. SPIE. 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications

KEYWORDS: Data modeling, Visualization, Tissues, Databases, Image segmentation, Feature extraction, Lung, Medical imaging, Computed tomography, Data analysis

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The analysis of large data sets can help to gain knowledge about specific organs or on specific diseases, just as big data analysis does in many non-medical areas. This article aims to gain information from 3D volumes, so the visual content of lung CT scans of a large number of patients. In the case of the described data set, only little annotation is available on the patients that were all part of an ongoing screening program and besides age and gender no information on the patient and the findings was available for this work. This is a scenario that can happen regularly as image data sets are produced and become available in increasingly large quantities but manual annotations are often not available and also clinical data such as text reports are often harder to share. We extracted a set of visual features from 12,414 CT scans of 9,348 patients that had CT scans of the lung taken in the context of a national lung screening program in Belarus. Lung fields were segmented by two segmentation algorithms and only cases where both algorithms were able to find left and right lung and had a Dice coefficient above 0.95 were analyzed. This assures that only segmentations of good quality were used to extract features of the lung. Patients ranged in age from 0 to 106 years. Data analysis shows that age can be predicted with a fairly high accuracy for persons under 15 years. Relatively good results were also obtained between 30 and 65 years where a steady trend is seen. For young adults and older people the results are not as good as variability is very high in these groups. Several visualizations of the data show the evolution patters of the lung texture, size and density with age. The experiments allow learning the evolution of the lung and the gained results show that even with limited metadata we can extract interesting information from large-scale visual data. These age-related changes (for example of the lung volume, the density histogram of the tissue) can also be taken into account for the interpretation of new cases. The database used includes patients that had suspicions on a chest X-ray, so it is not a group of healthy people, and only tendencies and not a model of a healthy lung at a specific age can be derived.

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