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Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results

Publication ,  Conference
Zhu, Z; Bashir, MR; Mazurowski, MA
Published in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
January 1, 2021

Medical images of a patient may have a significantly different appearance depending on imaging modality (e.g. MRI vs. CT), sequence type (e.g., T1-weighted MRI vs. T2-weighted MRI), and even manufacturer/model of equipment used for the same modality and sequence type (e.g. SIEMENS vs GE). Since in the context of deep learning training and test data often come from different institutions, it is important to determine how well neural networks generalize when image appearance varies. There is currently no systematic answer to this question. In this study, we investigate how deep neural networks trained for segmentation generalize. Our analysis is based on synthesizing a series of datasets of images with the target object of the same shape but with varying pixel intensity of the foreground object and the background. This simulates basic effects of changing equipment models and sequence types. We also consider scenarios when datasets with different image properties are combined to determine whether generalizability of the network to other scenarios is improved. We found that the generalizability of segmentation networks to changing intensities is poor. We also found that the generalizability is somewhat improved when different datasets are combined but that generalizability is typically limited to data similar to the two types of datasets included in training and not to datasets with different image intensities.

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Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510640238

Publication Date

January 1, 2021

Volume

11597
 

Citation

APA
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Zhu, Z., Bashir, M. R., & Mazurowski, M. A. (2021). Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 11597). https://doi.org/10.1117/12.2582190
Zhu, Z., M. R. Bashir, and M. A. Mazurowski. “Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results.” In Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 11597, 2021. https://doi.org/10.1117/12.2582190.
Zhu Z, Bashir MR, Mazurowski MA. Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2021.
Zhu, Z., et al. “Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results.” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11597, 2021. Scopus, doi:10.1117/12.2582190.
Zhu Z, Bashir MR, Mazurowski MA. Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2021.

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

ISBN

9781510640238

Publication Date

January 1, 2021

Volume

11597