Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
This work addresses the lack of annotations in medical imaging, enabling few-shot segmentation with reduced labeling effort, though it is incremental in adapting existing techniques.
The paper tackles the problem of few-shot semantic segmentation for medical images without requiring manual annotations during training, achieving superior performance over conventional methods that rely on annotations.
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications. Most of the existing FSS techniques require abundant annotated semantic classes for training. However, these methods may not be applicable for medical images due to the lack of annotations. To address this problem we make several contributions: (1) A novel self-supervised FSS framework for medical images in order to eliminate the requirement for annotations during training. Additionally, superpixel-based pseudo-labels are generated to provide supervision; (2) An adaptive local prototype pooling module plugged into prototypical networks, to solve the common challenging foreground-background imbalance problem in medical image segmentation; (3) We demonstrate the general applicability of the proposed approach for medical images using three different tasks: abdominal organ segmentation for CT and MRI, as well as cardiac segmentation for MRI. Our results show that, for medical image segmentation, the proposed method outperforms conventional FSS methods which require manual annotations for training.