# Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

arXiv: https://arxiv.org/abs/2211.08840 · HTML: https://mlsanity.com/paper/2211.08840/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 16 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [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)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Annotation-efficient deep learning for automatic medical image segmentation](https://mlsanity.com/paper/2012.04885/) (2020)
- [DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation](https://mlsanity.com/paper/1904.08465/) (2019)
- [Deep Co-Training for Semi-Supervised Image Segmentation](https://mlsanity.com/paper/1903.11233/) (2019)
- [Positional Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2106.09157/) (2021)
- [D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation](https://mlsanity.com/paper/1908.05104/) (2019)
- [Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-Supervised Learning](https://mlsanity.com/paper/2105.12722/) (2021)

## Built on by (most cited first, 3 of 3)

- [Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network](https://mlsanity.com/paper/2411.13873/) (2024)
- [Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation](https://mlsanity.com/paper/2312.10978/) (2023)
- [Weakly Supervised Pixel-Level Annotation with Visual Interpretability](https://mlsanity.com/paper/2502.17824/) (2025)

## Abstract (arXiv)

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.