| Autor: |
Rios Piedra EA; Department of Radiological Sciences at the University of California, Los Angeles, CA. Department of Bioengineering at the University of California, Los Angeles, CA. Medical Imaging Informatics (MII) at the University of California, Los Angeles, CA., Taira RK; Department of Radiological Sciences at the University of California, Los Angeles, CA. Department of Bioengineering at the University of California, Los Angeles, CA. Medical Imaging Informatics (MII) at the University of California, Los Angeles, CA., El-Saden S; Department of Radiological Sciences at the University of California, Los Angeles, CA. Department of Bioengineering at the University of California, Los Angeles, CA. Medical Imaging Informatics (MII) at the University of California, Los Angeles, CA. Department of Radiology, Veterans Administration Greater Los Angeles Healthcare, Los Angeles, CA., Ellingson BM; Department of Radiological Sciences at the University of California, Los Angeles, CA. Department of Bioengineering at the University of California, Los Angeles, CA. Department of Biomedical Physics and Neurology at the University of California, Los Angeles, CA., Bui AAT; Department of Radiological Sciences at the University of California, Los Angeles, CA. Department of Bioengineering at the University of California, Los Angeles, CA. Medical Imaging Informatics (MII) at the University of California, Los Angeles, CA., Hsu W; Department of Radiological Sciences at the University of California, Los Angeles, CA. Department of Bioengineering at the University of California, Los Angeles, CA. Medical Imaging Informatics (MII) at the University of California, Los Angeles, CA. |
| Abstrakt: |
Brain tumor analysis is moving towards volumetric assessment of magnetic resonance imaging (MRI), providing a more precise description of disease progression to better inform clinical decision-making and treatment planning. While a multitude of segmentation approaches exist, inherent variability in the results of these algorithms may incorrectly indicate changes in tumor volume. In this work, we present a systematic approach to characterize variability in tumor boundaries that utilizes equivalence tests as a means to determine whether a tumor volume has significantly changed over time. To demonstrate these concepts, 32 MRI studies from 8 patients were segmented using four different approaches (statistical classifier, region-based, edge-based, knowledge-based) to generate different regions of interest representing tumor extent. We showed that across all studies, the average Dice coefficient for the superset of the different methods was 0.754 (95% confidence interval 0.701-0.808) when compared to a reference standard. We illustrate how variability obtained by different segmentations can be used to identify significant changes in tumor volume between sequential time points. Our study demonstrates that variability is an inherent part of interpreting tumor segmentation results and should be considered as part of the interpretation process. |
