Radiomic Feature Stability Analysis Based on Probabilistic Segmentations
Haarburger C, Schock J, Truhn D, Weitz P, Mueller-Franzes G, Weninger L, Merhof D (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: IEEE Computer Society
Book Volume: 2020-April
Pages Range: 1188-1192
Conference Proceedings Title: Proceedings - International Symposium on Biomedical Imaging
Event location: Iowa City, IA, USA
ISBN: 9781538693308
DOI: 10.1109/ISBI45749.2020.9098674
Abstract
Identifying image features that are robust with respect to segmentation variability and domain shift is a tough challenge in radiomics. So far, this problem has mainly been tackled in test-retest analyses. In this work we analyze radiomics feature stability based on probabilistic segmentations. Based on a public lung cancer dataset, we generate an arbitrary number of plausible segmentations using a Probabilistic U-Net. From these segmentations, we extract a high number of plausible feature vectors for each lung tumor and analyze feature variance with respect to the segmentations. Our results suggest that there are groups of radiomic features that are more (e.g. statistics features) and less (e.g. gray-level size zone matrix features) robust against segmentation variability. Finally, we demonstrate that segmentation variance impacts the performance of a prognostic lung cancer survival model and propose a new and potentially more robust radiomics feature selection workflow.
Involved external institutions
Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
Germany (DE)
Karolinska Institute
Sweden (SE)
Germany (DE)
Karolinska Institute
Sweden (SE)
How to cite
APA:
Haarburger, C., Schock, J., Truhn, D., Weitz, P., Mueller-Franzes, G., Weninger, L., & Merhof, D. (2020). Radiomic Feature Stability Analysis Based on Probabilistic Segmentations. In Proceedings - International Symposium on Biomedical Imaging (pp. 1188-1192). Iowa City, IA, USA: IEEE Computer Society.
MLA:
Haarburger, Christoph, et al. "Radiomic Feature Stability Analysis Based on Probabilistic Segmentations." Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020, Iowa City, IA, USA IEEE Computer Society, 2020. 1188-1192.
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