Despite deep convolutional neural networks achieved impressive progress in
medical image computing and analysis, its paradigm of supervised learning
demands a large number of annotations for training to avoid overfitting and
achieving promising results. In clinical practices, massive semantic
annotations are difficult to acquire in some conditions where specialized
biomedical expert knowledge is required, and it is also a common condition
where only few annotated classes are available. In this work, we proposed a
novel method for few-shot medical image segmentation, which enables a
segmentation model to fast generalize to an unseen class with few training
images. We construct our few-shot image segmentor using a deep convolutional
network trained episodically. Motivated by the spatial consistency and
regularity in medical images, we developed an efficient global correlation
module to capture the correlation between a support and query image and
incorporate it into the deep network called global correlation network.
Moreover, we enhance discriminability of deep embedding to encourage clustering
of the feature domains of the same class while keep the feature domains of
different organs far apart. Ablation Study proved the effectiveness of the
proposed global correlation module and discriminative embedding loss. Extensive
experiments on anatomical abdomen images on both CT and MRI modalities are
performed to demonstrate the state-of-the-art performance of our proposed
model.

本文提出了一种基于全局相关模块和判别性嵌入损失的少样本医学图像分割方法，利用深度卷积神经网络高效准确地进行分割，取得了最新的实验成果。