Multi-modal segmentation with missing MR sequences using pre-trained fusion networks
This addresses missing data issues in retrospective medical imaging research, offering an incremental improvement for segmentation tasks.
The paper tackled the problem of missing imaging modalities in multi-modal segmentation by proposing a modified 3D UNet with dropout training, multi-pathway fusion, and pre-training, resulting in significant improvement on missing data and reduced memory usage in the BraTS challenge.
Missing data is a common problem in machine learning and in retrospective imaging research it is often encountered in the form of missing imaging modalities. We propose to take into account missing modalities in the design and training of neural networks, to ensure that they are capable of providing the best possible prediction even when multiple images are not available. The proposed network combines three modifications to the standard 3D UNet architecture: a training scheme with dropout of modalities, a multi-pathway architecture with fusion layer in the final stage, and the separate pre-training of these pathways. These modifications are evaluated incrementally in terms of performance on full and missing data, using the BraTS multi-modal segmentation challenge. The final model shows significant improvement with respect to the state of the art on missing data and requires less memory during training.