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Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

Publication ,  Journal Article
Yang, Z; Lafata, K; Vaios, E; Hu, Z; Mullikin, T; Fang Yin, F; Wang, C
Published in: Med Phys
September 11, 2023

BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. PURPOSE: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. METHODS: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). RESULTS: The developed SPU-Net model successfully achieved low uncertainty for correct segmentation predictions (e.g., tumor interior or healthy tissue interior) and high uncertainty for incorrect results (e.g., tumor boundaries). This model could allow the identification of missed tumor targets or segmentation errors in U-Net. Quantitatively, the SPU-Net model achieved the highest uncertainty scores for three segmentation targets (ET/TC/WT): 0.826/0.848/0.936, compared to 0.784/0.643/0.872 using the U-Net with TTA and 0.743/0.702/0.876 with the LSU-Net (scaling factor = 2). The SPU-Net also achieved statistically significantly higher Dice coefficients, underscoring the improved segmentation accuracy. CONCLUSION: The SPU-Net model offers a powerful tool to quantify glioma segmentation uncertainty while improving segmentation accuracy. The proposed method can be generalized to other medical image-related deep-learning applications for uncertainty evaluation.

Duke Scholars

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

September 11, 2023

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
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Yang, Z., Lafata, K., Vaios, E., Hu, Z., Mullikin, T., Fang Yin, F., & Wang, C. (2023). Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection. Med Phys. https://doi.org/10.1002/mp.16695
Yang, Zhenyu, Kyle Lafata, Eugene Vaios, Zongsheng Hu, Trey Mullikin, Fang- Fang Yin, and Chunhao Wang. “Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.Med Phys, September 11, 2023. https://doi.org/10.1002/mp.16695.
Yang Z, Lafata K, Vaios E, Hu Z, Mullikin T, Fang Yin F, et al. Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection. Med Phys. 2023 Sep 11;
Yang, Zhenyu, et al. “Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.Med Phys, Sept. 2023. Pubmed, doi:10.1002/mp.16695.
Yang Z, Lafata K, Vaios E, Hu Z, Mullikin T, Fang Yin F, Wang C. Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection. Med Phys. 2023 Sep 11;

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

September 11, 2023

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
  • 0903 Biomedical Engineering
  • 0299 Other Physical Sciences