Few-Shot Learning Techniques in Medical Image Segmentation

Few-shot learning addresses the scarcity of expert-annotated images by enabling segmentation models to generalise from only a handful of labelled examples. Broadly, three families of approaches have emerged: optimisation-based meta-learning, similarity-based prototypical methods and self-supervised or anomaly detection–inspired schemes. Meta-learning algorithms learn a set of initial parameters that can be rapidly fine-tuned on new organs or modalities with minimal data. Prototypical networks construct class prototypes from support images and guide query segmentation through similarity metrics, often augmented by spatial alignment or uncertainty estimation to refine boundaries. Self-supervised and anomaly detection approaches exploit intrinsic image structures—such as supervoxels or foreground representations—to bypass explicit background modelling and improve robustness. Recent innovations further integrate Kronecker-factored decompositions and specialised loss functions like average Hausdorff distance to encode morphological priors and accelerate convergence. Collectively, these techniques have demonstrated strong performance across MRI, CT and ultrasound applications, promising more efficient clinical workflows for tasks such as organ delineation, lesion detection and cross-institution adaptation.

Research from Nature Portfolio

One recent study introduced a bidirectional meta-Kronecker-factored optimiser within a model-agnostic meta-learning framework, combining Kronecker-factored decomposition with an average Hausdorff distance loss to capture organ shape precisely. This parameter-efficient approach adapts rapidly to novel MRI segmentation tasks using only a few support images, without requiring network architecture changes. Evaluated on an abdominal MRI dataset, it achieved average segmentation performance approaching 80% in few-shot settings and outperformed standard meta-learning baselines in both stability and computational cost.

Few-Shot Learning Techniques in Medical Image Segmentation publication trend

The graph below shows the total number of articles in few-shot learning techniques in medical image segmentation across all publications each year (not limited to Nature Index journals).

Technical terms

Few-shot learning: A paradigm that enables models to learn new segmentation tasks from a very limited number of labelled samples.

Meta-learning: A technique where a model learns a learning strategy or initial parameters that can be quickly adapted to novel tasks with minimal data.

Prototypical learning: A similarity-based approach that represents each class by an embedding prototype computed from support examples to guide query segmentation.

Supervoxel: A cluster of adjacent voxels in 3D images sharing similar intensity or texture, used in self-supervised tasks to capture local anatomical structures.

Kronecker-factored decomposition: A mathematical factorisation that expresses large parameter matrices as Kronecker products, improving optimisation efficiency and numerical stability.

Hausdorff distance loss: A boundary-aware loss function that penalises the greatest distance between predicted and ground-truth contours, enforcing morphological accuracy.

References

1. A systematic review of few-shot learning in medical imaging. Artificial Intelligence in Medicine (2024).
2. Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Medical Image Analysis (2022).
3. Meta-learning with implicit gradients in a few-shot setting for medical image segmentation. Computers in Biology and Medicine (2022).
4. Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration. Medical Image Analysis (2023).
5. Bidirectional meta-Kronecker factored optimizer and Hausdorff distance loss for few-shot medical image segmentation. Scientific Reports (2023).