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| Home > Publications database > Advanced Automated Model for Robust Bone Marrow Segmentation in Whole-body MRI. |
| Journal Article | DKFZ-2025-00208 |
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2025
Elsevier
Philadelphia, PA [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.acra.2024.12.060
Abstract: To establish an advanced automated bone marrow (BM) segmentation model on whole-body (WB-)MRI in monoclonal plasma cell disorders (MPCD), and to demonstrate its robust performance on multicenter datasets with severe myeloma-related pathologies.The study cohort comprised multi-vendor, multi-protocol imaging data acquired with varying field strength across 8 different centers. In total, 210 WB-MRIs of 207 MPCD patients were included. An nnU-Net algorithm was established for segmenting the individual bone marrow spaces (BMS) of the spine, pelvis, humeri and femora (advanced segmentation model). For this task, 186 T1-weighted (T1w) WB-MRIs from center 1 were used in the training set. Test sets included 12 T1w WB-MRIs from center 2 (I) and 9 T1w WB-MRIs from centers 3-8 (II). Example cases were included to showcase segmentation performance on T1w WB-MRIs with extensive tumor load. The segmentation accuracy of the advanced segmentation model was compared to a prior established basic segmentation model by calculating Dice scores and using the Wilcoxon signed-rank test.The mean Dice score on the individual BMS was 0.89±0.13 (test set I) and 0.88±0.11 (test set II), significantly higher than the Dice scores of a prior basic model (p<0.05). Dice scores for the BMS of the individual bones ranged from 0.77 to 0.96 (test set I), and 0.81 to 0.95 (test set II). BM altered by myeloma-relevant pathologies, artifacts or low imaging quality was precisely segmented.The advanced model performed reliable, automated segmentations, even on heterogeneously acquired multicenter WB-MRIs with severe pathologies.
Keyword(s): Deep Learning ; Magnetic Resonance Imaging ; Multiple Myeloma ; Segmentation
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