Skull stripping with purely synthetic data
This work presents a new direction for generalizable medical image segmentation, though it is incremental in its specific application.
The paper tackled the problem of skull stripping in medical imaging without using real brain images or labels, achieving comparable accuracy across multi-modal, multi-species, and pathological cases.
While many skull stripping algorithms have been developed for multi-modal and multi-species cases, there is still a lack of a fundamentally generalizable approach. We present PUMBA(PUrely synthetic Multimodal/species invariant Brain extrAction), a strategy to train a model for brain extraction with no real brain images or labels. Our results show that even without any real images or anatomical priors, the model achieves comparable accuracy in multi-modal, multi-species and pathological cases. This work presents a new direction of research for any generalizable medical image segmentation task.