Evaluation of multiparametric MRI for deep learning-based segmentation of Wilms tumor

Myrthe A. D. Buser; Marc H. W. A. Wijnen; Alida F. W. van der Steeg; Bas H. M. van der Velden

doi:10.1117/12.2653552

3 April 2023 Evaluation of multiparametric MRI for deep learning-based segmentation of Wilms tumor

Myrthe A. D. Buser, Marc H. W. A. Wijnen, Alida F. W. van der Steeg, Bas H. M. van der Velden

Proceedings Volume 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment; 1246712 (2023) https://doi.org/10.1117/12.2653552
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Deep learning techniques to segment Wilms tumor typically use a single MRI sequence as input. The aim of this study was to assess whether multiparametric MRI input improves Wilms tumor segmentation. 45 patients were consecutively included, of which 36 were used for training and nine for testing. All seven input combinations of postcontrast T₁-weighted imaging, T₂-weighted imaging, and diffusion weighted imaging (DWI) were used for nnU-Net training. Dice scores and the 95th percentile of the Haussdorf distance (HD95) were used to evaluate the input combinations. The median Dice score was highest when combining all MRI sequences (Dice = 0.93), the median HD95 was lowest when combining postcontrast T₁-weighted imaging and DWI (HD95 = 5.4 mm). Single-parametric DWI input performed significantly worse than other input combinations (median Dice = 0.64, median HD95 = 29.5 mm, p = 0.004). All other combinations, including standalone sequences, showed similar performance to each other. Our results suggest that adding sequences to standalone T₁-weighted or T₂-weighted imaging does not significantly improve segmentation results.

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Myrthe A. D. Buser, Marc H. W. A. Wijnen, Alida F. W. van der Steeg, and Bas H. M. van der Velden "Evaluation of multiparametric MRI for deep learning-based segmentation of Wilms tumor", Proc. SPIE 12467, Medical Imaging 2023: Image Perception, Observer Performance, and Technology Assessment, 1246712 (3 April 2023); https://doi.org/10.1117/12.2653552

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