  The prevailing deep learning-based methods of predicting cardiac segmentation
involve reconstructed magnetic resonance (MR) images. The heavy dependency of
segmentation approaches on image quality significantly limits the acceleration
rate in fast MR reconstruction. Moreover, the practice of treating
reconstruction and segmentation as separate sequential processes leads to
artifact generation and information loss in the intermediate stage. These
issues pose a great risk to achieving high-quality outcomes. To leverage the
redundant k-space information overlooked in this dual-step pipeline, we
introduce a novel approach to directly deriving segmentations from sparse
k-space samples using a transformer (DiSK). DiSK operates by globally
extracting latent features from 2D+time k-space data with attention blocks and
subsequently predicting the segmentation label of query points. We evaluate our
model under various acceleration factors (ranging from 4 to 64) and compare
against two image-based segmentation baselines. Our model consistently
outperforms the baselines in Dice and Hausdorff distances across foreground
classes for all presented sampling rates.
