  The application of deep learning to medical image segmentation has been
hampered due to the lack of abundant pixel-level annotated data. Few-shot
Semantic Segmentation (FSS) is a promising strategy for breaking the deadlock.
However, a high-performing FSS model still requires sufficient pixel-level
annotated classes for training to avoid overfitting, which leads to its
performance bottleneck in medical image segmentation due to the unmet need for
annotations. Thus, semi-supervised FSS for medical images is accordingly
proposed to utilize unlabeled data for further performance improvement.
Nevertheless, existing semi-supervised FSS methods has two obvious defects: (1)
neglecting the relationship between the labeled and unlabeled data; (2) using
unlabeled data directly for end-to-end training leads to degenerated
representation learning. To address these problems, we propose a novel
semi-supervised FSS framework for medical image segmentation. The proposed
framework employs Poisson learning for modeling data relationship and
propagating supervision signals, and Spatial Consistency Calibration for
encouraging the model to learn more coherent representations. In this process,
unlabeled samples do not involve in end-to-end training, but provide
supervisory information for query image segmentation through graph-based
learning. We conduct extensive experiments on three medical image segmentation
datasets (i.e. ISIC skin lesion segmentation, abdominal organs segmentation for
MRI and abdominal organs segmentation for CT) to demonstrate the
state-of-the-art performance and broad applicability of the proposed framework.
