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Abstract #2162

Automatic segmentation of whole-body MRI using UnnU-Net: Feasibility of whole-skeleton ADC evaluation in plasma cell disorders

Renyang Gu1, Michela Antonelli2, Pritesh Mehta 3, Ashik Amlani 4, Adrian Green4, Radhouene Neji 5, Sebastien Ourselin2, Isabel Dregely2, and Vicky Goh2
1Department of Biomedical Engineering and Imaging Science, King's College London, London, United Kingdom, 2School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, 3University College London, London, United Kingdom, 4Department of Radiology, Guy’s & St Thomas’ NHS Foundation Trust, London, United Kingdom, 5Siemens Healthcare Limited, London, United Kingdom


Multiple myeloma is a heterogeneous bone marrow cancer. Assessment of changes in mean apparent diffusion coefficient (ADCmean) is helpful for evaluating treatment response but has not been feasible for the whole skeleton due to the time-consuming nature of manual segmentation. Whole-skeleton and per-station ADCmean were quantified from whole-body MRI using automated segmentation by an uncertainty-aware nnU-Net in 30 patients with plasma cell disorders and compared against the manual segmentation by radiologists. No differences were observed in whole-skeleton or per-station ADCmean when using the automatic and manual segmentations. Further investigation is required in a larger dataset, but initial results are promising.

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