# Bidirectional RNN-based Few Shot Learning for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2011.09608 · HTML: https://mlsanity.com/paper/2011.09608/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification](https://mlsanity.com/paper/2203.13465/) (2022)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [A Meta-Learning Approach for Medical Image Registration](https://mlsanity.com/paper/2104.10447/) (2021)
- [Robust Prototypical Few-Shot Organ Segmentation with Regularized Neural-ODEs](https://mlsanity.com/paper/2208.12428/) (2022)
- [Deep Transfer Learning for Kidney Cancer Diagnosis](https://mlsanity.com/paper/2408.04318/) (2024)
- [Few-Shot 3D Volumetric Segmentation with Multi-Surrogate Fusion](https://mlsanity.com/paper/2408.14427/) (2024)
- [MPBD-LSTM: A Predictive Model for Colorectal Liver Metastases Using Time Series Multi-phase Contrast-Enhanced CT Scans](https://mlsanity.com/paper/2412.01973/) (2024)
- [Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance](https://mlsanity.com/paper/2210.14645/) (2022)
- [Adversarial RAW: Image-Scaling Attack Against Imaging Pipeline](https://mlsanity.com/paper/2206.01733/) (2022)

## Abstract (arXiv)

Segmentation of organs of interest in 3D medical images is necessary for accurate diagnosis and longitudinal studies. Though recent advances using deep learning have shown success for many segmentation tasks, large datasets are required for high performance and the annotation process is both time consuming and labor intensive. In this paper, we propose a 3D few shot segmentation framework for accurate organ segmentation using limited training samples of the target organ annotation. To achieve this, a U-Net like network is designed to predict segmentation by learning the relationship between 2D slices of support data and a query image, including a bidirectional gated recurrent unit (GRU) that learns consistency of encoded features between adjacent slices. Also, we introduce a transfer learning method to adapt the characteristics of the target image and organ by updating the model before testing with arbitrary support and query data sampled from the support data. We evaluate our proposed model using three 3D CT datasets with annotations of different organs. Our model yielded significantly improved performance over state-of-the-art few shot segmentation models and was comparable to a fully supervised model trained with more target training data.