# Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

arXiv: https://arxiv.org/abs/1907.02757 · HTML: https://mlsanity.com/paper/1907.02757/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 30 of 70)

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- [Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation](https://mlsanity.com/paper/2112.09645/) (2021)
- [Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels](https://mlsanity.com/paper/2203.02048/) (2022)
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- [Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes](https://mlsanity.com/paper/2403.16499/) (2024)
- [Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directions](https://mlsanity.com/paper/2303.12484/) (2023)
- [A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation](https://mlsanity.com/paper/2107.04934/) (2021)
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- [A Review of Predictive and Contrastive Self-supervised Learning for Medical Images](https://mlsanity.com/paper/2302.05043/) (2023)
- [Unsupervised pre-training of graph transformers on patient population graphs](https://mlsanity.com/paper/2207.10603/) (2022)
- [Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound](https://mlsanity.com/paper/2208.10642/) (2022)
- [Understand and Improve Contrastive Learning Methods for Visual Representation: A Review](https://mlsanity.com/paper/2106.03259/) (2021)
- [Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives](https://mlsanity.com/paper/2106.15707/) (2021)
- [DeepMCAT: Large-Scale Deep Clustering for Medical Image Categorization](https://mlsanity.com/paper/2110.00109/) (2021)
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## Abstract (arXiv)

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net.