Brain Tumor Segmentation on MRI with Missing Modalities
This addresses a practical problem in medical imaging for clinicians, but it is incremental as it builds on existing segmentation methods to handle missing data.
The paper tackles brain tumor segmentation from MRI when some imaging modalities are missing, a common clinical issue, by proposing a network with channel-independent encoding and feature-fusion decoding, achieving results that show segmentation quality varies based on the missing modality and align with expert screening practices.
Brain Tumor Segmentation from magnetic resonance imaging (MRI) is a critical technique for early diagnosis. However, rather than having complete four modalities as in BraTS dataset, it is common to have missing modalities in clinical scenarios. We design a brain tumor segmentation algorithm that is robust to the absence of any modality. Our network includes a channel-independent encoding path and a feature-fusion decoding path. We use self-supervised training through channel dropout and also propose a novel domain adaptation method on feature maps to recover the information from the missing channel. Our results demonstrate that the quality of the segmentation depends on which modality is missing. Furthermore, we also discuss and visualize the contribution of each modality to the segmentation results. Their contributions are along well with the expert screening routine.