  Deep neural networks enable highly accurate image segmentation, but require
large amounts of manually annotated data for supervised training. Few-shot
learning aims to address this shortcoming by learning a new class from a few
annotated support examples. We introduce, a novel few-shot framework, for the
segmentation of volumetric medical images with only a few annotated slices.
Compared to other related works in computer vision, the major challenges are
the absence of pre-trained networks and the volumetric nature of medical scans.
We address these challenges by proposing a new architecture for few-shot
segmentation that incorporates 'squeeze & excite' blocks. Our two-armed
architecture consists of a conditioner arm, which processes the annotated
support input and generates a task-specific representation. This representation
is passed on to the segmenter arm that uses this information to segment the new
query image. To facilitate efficient interaction between the conditioner and
the segmenter arm, we propose to use 'channel squeeze & spatial excitation'
blocks - a light-weight computational module - that enables heavy interaction
between both the arms with negligible increase in model complexity. This
contribution allows us to perform image segmentation without relying on a
pre-trained model, which generally is unavailable for medical scans.
Furthermore, we propose an efficient strategy for volumetric segmentation by
optimally pairing a few slices of the support volume to all the slices of the
query volume. We perform experiments for organ segmentation on whole-body
contrast-enhanced CT scans from the Visceral Dataset. Our proposed model
outperforms multiple baselines and existing approaches with respect to the
segmentation accuracy by a significant margin. The source code is available at
https://github.com/abhi4ssj/few-shot-segmentation.
