bibtext: @InProceedings{ Wal_Multisequence_MICCAI2024,
                   author = { Walsh, Ricky and Gaubert, Malo and Meurée, Cédric and Hussein, Burhan Rashid and Kerbrat, Anne and Casey, Romain and Combès, Benoit and Galassi, Francesca },
                   title = { { Multi-sequence learning for multiple sclerosis lesion segmentation in spinal cord MRI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: Automated tools developed to detect multiple sclerosis lesions in spinal cord MRI have thus far been based on processing single MR sequences in a deep learning model. This study is the first to explore a multi-sequence approach to this task and we propose a method to address inherent issues in multi-sequence spinal cord data, i.e., differing fields of view, inter-sequence alignment and incomplete sequence data for training and inference. In particular, we investigate a simple missing-modality method of replacing missing features with the mean over the available sequences. This approach leads to better segmentation results when processing a single sequence at inference than a model trained directly on that sequence, and our experiments provide valuable insights into the mechanism underlying this surprising result. In particular, we demonstrate that both the encoder and decoder benefit from the variability introduced in the multi-sequence setting. Additionally, we propose a latent feature augmentation scheme to reproduce this variability in a single-sequence setting, resulting in similar improvements over the single-sequence baseline.
title: Multi-sequence learning for multiple sclerosis lesion segmentation in spinal cord MRI
authors: Walsh, Ricky Gaubert, Malo Meurée, Cédric Hussein, Burhan Rashid Kerbrat, Anne Casey, Romain Combès, Benoit Galassi, Francesca