  Segmentation of organs of interest in 3D medical images is necessary for
accurate diagnosis and longitudinal studies. Though recent advances using deep
learning have shown success for many segmentation tasks, large datasets are
required for high performance and the annotation process is both time consuming
and labor intensive. In this paper, we propose a 3D few shot segmentation
framework for accurate organ segmentation using limited training samples of the
target organ annotation. To achieve this, a U-Net like network is designed to
predict segmentation by learning the relationship between 2D slices of support
data and a query image, including a bidirectional gated recurrent unit (GRU)
that learns consistency of encoded features between adjacent slices. Also, we
introduce a transfer learning method to adapt the characteristics of the target
image and organ by updating the model before testing with arbitrary support and
query data sampled from the support data. We evaluate our proposed model using
three 3D CT datasets with annotations of different organs. Our model yielded
significantly improved performance over state-of-the-art few shot segmentation
models and was comparable to a fully supervised model trained with more target
training data.
