  Recent work has shown that label-efficient few-shot learning through
self-supervision can achieve promising medical image segmentation results.
However, few-shot segmentation models typically rely on prototype
representations of the semantic classes, resulting in a loss of local
information that can degrade performance. This is particularly problematic for
the typically large and highly heterogeneous background class in medical image
segmentation problems. Previous works have attempted to address this issue by
learning additional prototypes for each class, but since the prototypes are
based on a limited number of slices, we argue that this ad-hoc solution is
insufficient to capture the background properties. Motivated by this, and the
observation that the foreground class (e.g., one organ) is relatively
homogeneous, we propose a novel anomaly detection-inspired approach to few-shot
medical image segmentation in which we refrain from modeling the background
explicitly. Instead, we rely solely on a single foreground prototype to compute
anomaly scores for all query pixels. The segmentation is then performed by
thresholding these anomaly scores using a learned threshold. Assisted by a
novel self-supervision task that exploits the 3D structure of medical images
through supervoxels, our proposed anomaly detection-inspired few-shot medical
image segmentation model outperforms previous state-of-the-art approaches on
two representative MRI datasets for the tasks of abdominal organ segmentation
and cardiac segmentation.
