# Self-Supervised Alignment Learning for Medical Image Segmentation

arXiv: https://arxiv.org/abs/2406.15699 · HTML: https://mlsanity.com/paper/2406.15699/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 12 of 12)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [Contrastive learning of global and local features for medical image segmentation with limited annotations](https://mlsanity.com/paper/2006.10511/) (2020)
- [Corrupted Image Modeling for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2202.03382/) (2022)
- [Crafting Better Contrastive Views for Siamese Representation Learning](https://mlsanity.com/paper/2202.03278/) (2022)
- [Bootstrapped Masked Autoencoders for Vision BERT Pretraining](https://mlsanity.com/paper/2207.07116/) (2022)
- [Positional Contrastive Learning for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2106.09157/) (2021)
- [Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels](https://mlsanity.com/paper/2107.13741/) (2021)
- [Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis](https://mlsanity.com/paper/2206.04281/) (2022)
- [Attentive Symmetric Autoencoder for Brain MRI Segmentation](https://mlsanity.com/paper/2209.08887/) (2022)

## Built on by (most cited first, 7 of 7)

- [Structure-aware Semantic Discrepancy and Consistency for 3D Medical Image Self-supervised Learning](https://mlsanity.com/paper/2507.02581/) (2025)
- [Fire360: A Benchmark for Robust Perception and Episodic Memory in Degraded 360-Degree Firefighting Videos](https://mlsanity.com/paper/2506.02167/) (2025)
- [Resource-efficient medical image classification for edge devices](https://mlsanity.com/paper/2512.17515/) (2025)
- [FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation](https://mlsanity.com/paper/2507.20056/) (2025)
- [Coordinative Learning with Ordinal and Relational Priors for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2511.11276/) (2025)
- [Scale-Aware Self-Supervised Learning for Segmentation of Small and Sparse Structures](https://mlsanity.com/paper/2601.18619/) (2026)
- [Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM](https://mlsanity.com/paper/2604.23314/) (2026)

## Abstract (arXiv)

Recently, self-supervised learning (SSL) methods have been used in pre-training the segmentation models for 2D and 3D medical images. Most of these methods are based on reconstruction, contrastive learning and consistency regularization. However, the spatial correspondence of 2D slices from a 3D medical image has not been fully exploited. In this paper, we propose a novel self-supervised alignment learning framework to pre-train the neural network for medical image segmentation. The proposed framework consists of a new local alignment loss and a global positional loss. We observe that in the same 3D scan, two close 2D slices usually contain similar anatomic structures. Thus, the local alignment loss is proposed to make the pixel-level features of matched structures close to each other. Experimental results show that the proposed alignment learning is competitive with existing self-supervised pre-training approaches on CT and MRI datasets, under the setting of limited annotations.