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Abstract #2409

Using uncertainty estimation to increase the robustness of bone marrow segmentation in T1-weighted Dixon MRI for multiple myeloma

Renyang Gu1, Michela Antonelli1, Pritesh Mehta 2, Ashik Amlani 3, Adrian Green3, Radhouene Neji 4, Sebastien Ourselin1, Isabel Dregely1, and Vicky Goh1
1School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, 2Biomedical Engineering and Medical Physics, University College London, London, United Kingdom, 3Radiology, Guy’s and St Thomas’ Hospitals, London, United Kingdom, 4Siemens Healthcare Limited, Frimley, United Kingdom

Reliable skeletal segmentation of T1-weighted Dixon MRI is a first step towards measuring marrow fat-fraction as a surrogate metric for early marrow infiltration. We proposed an uncertainty-aware 2D U-Net (uU-Net) to reduce the impact of noisy ground-truth labels on segmentation accuracy. Five-fold cross-validation on a dataset of 30 myeloma patients provided a mean ± SD Dice coefficient of 0.74 ± 0.03 (vs. 0.73 ± 0.04, U-Net) and 0.63 ± 0.03 (vs 0.62 ± 0.04, U-Net) for pelvic and abdominal stations, respectively. Of clinical importance, improved segmentation of the ilium and vertebrae were achieved.

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