Keywords: Longitudinal prediction, asymmetric cascade U-Net, Multiple Myeloma, bone lesion assessment
TL;DR: This work provides an asymmetric cascade U-Net architecture for the prediction of future focal bone lesions using longitudinal whole body MRI images of multiple myeloma patients
Abstract: The reliable and timely stratification of bone lesion evolution risk in smoldering Multiple Myeloma plays an important role in identifying prime markers of the disease’s advance and in improving the patients’ outcome. In this work we provide an asymmetric cascade network for the longitudinal prediction of future bone lesions for T1 weighted whole body MR images. The proposed cascaded architecture, consisting of two distinct configured U-Nets, first detects the bone regions and subsequently predicts lesions within bones in a patch based way. The algorithm provides a full volumetric risk score map for the identification of early signatures of emerging lesions and for visualising high risk locations. The prediction accuracy is evaluated on a longitudinal dataset of 63 multiple myeloma patients.
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