HeMIS: Hetero-Modal Image Segmentation
This addresses the challenge of missing data in multi-modal medical imaging for clinicians and researchers, offering a practical solution without combinatorial imputation models, though it is incremental in its approach.
The authors tackled the problem of robust medical image segmentation with missing modalities by learning a shared latent space for each modality, enabling averaging over available inputs without imputation. The method achieved state-of-the-art results on brain tumor and MS lesion MRI datasets with all modalities and showed graceful performance degradation as modalities were removed, outperforming alternatives.
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.