Mr. Ken Chang Profile

Ken Chang

Grad Student at Athinoula A Martinos Ctr for Biomedical Imaging

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Publications (6)

Proceedings Article | 4 April 2022 Paper

Is this good enough? On expert perception of brain tumor segmentation quality

Katharina Hoebel, Christopher Bridge, Sara Ahmed, Oluwatosin Akintola, Caroline Chung, Raymond Huang, Jason Johnson, Albert Kim, K. Ina Ly, Ken Chang, Jay Patel, Marco Pinho, Tracy Batchelor, Bruce Rosen, Elizabeth Gerstner, Jayashree Kalpathy-Cramer

Proceedings Volume 12035, 120350P (2022) https://doi.org/10.1117/12.2611810

KEYWORDS: Image segmentation, Tumors, Brain, Neuroimaging, Statistical modeling, Radiology, Radiation oncology, Medicine, Magnetic resonance imaging

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Proceedings Article | 4 April 2022 Presentation + Paper

Do I know this? segmentation uncertainty under domain shift

Katharina Hoebel, Christopher Bridge, Andreanne Lemay, Ken Chang, Jay Patel, Bruce Rosen, Jayashree Kalpathy-Cramer

Proceedings Volume 12032, 1203211 (2022) https://doi.org/10.1117/12.2611867

KEYWORDS: Data modeling, Image segmentation, Performance modeling, Brain, Tumors, Statistical modeling, Photography, Medical imaging, Statistical analysis, Error analysis

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The communication of reliable uncertainty estimates is crucial in the effort towards increasing trust in Deep Learning applications for medical image analysis. Importantly, reliable uncertainty estimates should remain stable under naturally occurring domain shifts. In this study, we evaluate the relationship between epistemic uncertainty and segmentation quality under domain shift within two clinical contexts: optic disc segmentation in retinal photographs and brain tumor segmentation from multi-modal brain MRI. Specifically, we assess the behavior of two epistemic uncertainty metrics derived from i, a single UNet’s sigmoid predictions, ii, deep ensembles, and iii, Monte Carlo dropout UNets, each trained with both soft Dice and weighted cross-entropy loss. Domain shifts were modeled by excluding a group with a known characteristic (glaucoma for optic disc segmentation and low-grade glioma for brain tumor segmentation) from model development and using the excluded data as additional, domain-shifted test data. While the performance of all models dropped slightly on the domain-shifted test data compared to the in-domain test set, there was no change in the Pearson correlation coefficient between the uncertainty metrics and the Dice scores of the segmentations. However, we did observe differences in the performance of two quality assessment applications based on epistemic uncertainty between the segmentation tasks. We introduce a new metric, the empirical strength distribution, to better describe the strength of the relationship between segmentation performance and epistemic uncertainty on a dataset level. We found that failures of the studied quality assessment applications were largely caused by shifts in the empirical strength distributions between training, in-domain, and domain-shifted test datasets. In conclusion, quality assessment tools based on the strong relationship between epistemic uncertainty and segmentation quality can be stable under small domain shifts. Developers should thoroughly evaluate the strength relationships for all available data and, if possible, under domain shift to ensure the validity of these uncertainty estimates on unseen data.

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

Proceedings Volume 11313, 113131K (2020) https://doi.org/10.1117/12.2548722

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

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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

Proceedings Volume 10573, 105732V (2018) https://doi.org/10.1117/12.2294961

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

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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

Proceedings Volume 10579, 105790Q (2018) https://doi.org/10.1117/12.2295942

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

Proceedings Volume 10574, 1057433 (2018) https://doi.org/10.1117/12.2293941

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

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Showing 5 of 6 publications

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