# Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation

arXiv: https://arxiv.org/abs/2312.10978 · HTML: https://mlsanity.com/paper/2312.10978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/1907.07034/) (2019)
- [Interpolation Consistency Training for Semi-Supervised Learning](https://mlsanity.com/paper/1903.03825/) (2019)
- [Annotation-efficient deep learning for automatic medical image segmentation](https://mlsanity.com/paper/2012.04885/) (2020)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2110.08762/) (2021)
- [Deep Co-Training for Semi-Supervised Image Segmentation](https://mlsanity.com/paper/1903.11233/) (2019)
- [Deep learning in radiology: an overview of the concepts and a survey of the state of the art](https://mlsanity.com/paper/1802.08717/) (2018)
- [D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation](https://mlsanity.com/paper/1908.05104/) (2019)
- [Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness](https://mlsanity.com/paper/1905.11427/) (2019)
- [Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning](https://mlsanity.com/paper/2105.12722/) (2021)
- [Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation](https://mlsanity.com/paper/2211.08840/) (2022)
- [Suggestive Annotation of Brain MR Images with Gradient-guided Sampling](https://mlsanity.com/paper/2206.01014/) (2022)

## Built on by (most cited first, 1 of 1)

- [MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal Prostate MRI Segmentation](https://mlsanity.com/paper/2510.17529/) (2025)

## Abstract (arXiv)

Background: Deep learning has presented great potential in accurate MR image segmentation when enough labeled data are provided for network optimization. However, manually annotating 3D MR images is tedious and time-consuming, requiring experts with rich domain knowledge and experience. Purpose: To build a deep learning method exploring sparse annotations, namely only a single 2D slice label for each 3D training MR image. Population: 3D MR images of 150 subjects from two publicly available datasets were included. Among them, 50 (1,377 image slices) are for prostate segmentation. The other 100 (8,800 image slices) are for left atrium segmentation. Five-fold cross-validation experiments were carried out utilizing the first dataset. For the second dataset, 80 subjects were used for training and 20 were used for testing. Assessment: A collaborative learning method by integrating the strengths of semi-supervised and self-supervised learning schemes was developed. The method was trained using labeled central slices and unlabeled non-central slices. Segmentation performance on testing set was reported quantitatively and qualitatively. Results: Compared to FS-LCS, MT, UA-MT, DCT-Seg, ICT, and AC-MT, the proposed method achieved a substantial improvement in segmentation accuracy, increasing the mean B-IoU significantly by more than 10.0% for prostate segmentation (proposed method B-IoU: 70.3% vs. ICT B-IoU: 60.3%) and by more than 6.0% for left atrium segmentation (proposed method B-IoU: 66.1% vs. ICT B-IoU: 60.1%).