AUTHOR=Hitziger Sebastian , Ling Wen Xin , Fritz Thomas , D'Albis Tiziano , Lemke Andreas , Grilo Joana TITLE=Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies JOURNAL=Frontiers in Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.964250 DOI=10.3389/fnins.2022.964250 ISSN=1662-453X ABSTRACT=

We present a deep learning method for the segmentation of new lesions in longitudinal FLAIR MRI sequences acquired at two different time points. In our approach, the 3D volumes are processed slice-wise across the coronal, axial, and sagittal planes and the predictions from the three orientations are merged using an optimized voting strategy. Our method achieved best F1 score (0.541) among all participating methods in the MICCAI 2021 challenge Multiple sclerosis new lesions segmentation (MSSEG-2). Moreover, we show that our method is on par with the challenge's expert neuroradiologists: on an unbiased ground truth, our method achieves results comparable to those of the four experts in terms of detection (F1 score) and segmentation accuracy (Dice score).