  Despite the tremendous success of deep neural networks in medical image
segmentation, they typically require a large amount of costly, expert-level
annotated data. Few-shot segmentation approaches address this issue by learning
to transfer knowledge from limited quantities of labeled examples.
Incorporating appropriate prior knowledge is critical in designing
high-performance few-shot segmentation algorithms. Since strong spatial priors
exist in many medical imaging modalities, we propose a prototype-based method
-- namely, the location-sensitive local prototype network -- that leverages
spatial priors to perform few-shot medical image segmentation. Our approach
divides the difficult problem of segmenting the entire image with global
prototypes into easily solvable subproblems of local region segmentation with
local prototypes. For organ segmentation experiments on the VISCERAL CT image
dataset, our method outperforms the state-of-the-art approaches by 10% in the
mean Dice coefficient. Extensive ablation studies demonstrate the substantial
benefits of incorporating spatial information and confirm the effectiveness of
our approach.
