# Few-shot image segmentation for cross-institution male pelvic organs using registration-assisted prototypical learning

arXiv: https://arxiv.org/abs/2201.06358 · HTML: https://mlsanity.com/paper/2201.06358/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 17 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation](https://mlsanity.com/paper/2007.09886/) (2020)
- ['Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images](https://mlsanity.com/paper/1902.01314/) (2019)
- [USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets](https://mlsanity.com/paper/1904.08254/) (2019)
- [A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2103.10178/) (2021)
- [Federated Whole Prostate Segmentation in MRI with Personalized Neural Architectures](https://mlsanity.com/paper/2107.08111/) (2021)
- [Automatic Prostate Zonal Segmentation Using Fully Convolutional Network with Feature Pyramid Attention](https://mlsanity.com/paper/1911.00127/) (2019)

## Built on by (most cited first, 6 of 6)

- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Few Shot Medical Image Segmentation with Cross Attention Transformer](https://mlsanity.com/paper/2303.13867/) (2023)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [Active learning using adaptable task-based prioritisation](https://mlsanity.com/paper/2212.01703/) (2022)
- [Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs](https://mlsanity.com/paper/2208.12428/) (2022)
- [Tell2Reg: Establishing spatial correspondence between images by the same language prompts](https://mlsanity.com/paper/2502.03118/) (2025)

## Abstract (arXiv)

The ability to adapt medical image segmentation networks for a novel class such as an unseen anatomical or pathological structure, when only a few labelled examples of this class are available from local healthcare providers, is sought-after. This potentially addresses two widely recognised limitations in deploying modern deep learning models to clinical practice, expertise-and-labour-intensive labelling and cross-institution generalisation. This work presents the first 3D few-shot interclass segmentation network for medical images, using a labelled multi-institution dataset from prostate cancer patients with eight regions of interest. We propose an image alignment module registering the predicted segmentation of both query and support data, in a standard prototypical learning algorithm, to a reference atlas space. The built-in registration mechanism can effectively utilise the prior knowledge of consistent anatomy between subjects, regardless whether they are from the same institution or not. Experimental results demonstrated that the proposed registration-assisted prototypical learning significantly improved segmentation accuracy (p-values<0.01) on query data from a holdout institution, with varying availability of support data from multiple institutions. We also report the additional benefits of the proposed 3D networks with 75% fewer parameters and an arguably simpler implementation, compared with existing 2D few-shot approaches that segment 2D slices of volumetric medical images.