Sparse annotation strategies for segmentation of short axis cardiac MRI
Josh Stein Maxime Di Folco Julia Schnabel
Short axis cardiac MRI segmentation is a well-researched topic, with
excellent results achieved by state-of-the-art models in a supervised setting.
However, annotating MRI volumes is time-consuming and expensive. Many different
approaches (e.g. transfer learning, data augmentation, few-shot learning, etc.)
have emerged in an effort to use fewer annotated data and still achieve similar
performance as a fully supervised model. Nevertheless, to the best of our
knowledge, none of these works focus on which slices of MRI volumes are most
important to annotate for yielding the best segmentation results. In this
paper, we investigate the effects of training with sparse volumes, i.e.
reducing the number of cases annotated, and sparse annotations, i.e. reducing
the number of slices annotated per case. We evaluate the segmentation
performance using the state-of-the-art nnU-Net model on two public datasets to
identify which slices are the most important to annotate. We have shown that
training on a significantly reduced dataset (48 annotated volumes) can give a
Dice score greater than 0.85 and results comparable to using the full dataset
(160 and 240 volumes for each dataset respectively). In general, training on
more slice annotations provides more valuable information compared to training
on more volumes. Further, annotating slices from the middle of volumes yields
the most beneficial results in terms of segmentation performance, and the
apical region the worst. When evaluating the trade-off between annotating
volumes against slices, annotating as many slices as possible instead of
annotating more volumes is a better strategy.