# Contrastive Registration for Unsupervised Medical Image Segmentation

arXiv: https://arxiv.org/abs/2011.08894 · HTML: https://mlsanity.com/paper/2011.08894/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 19 of 19)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics](https://mlsanity.com/paper/2111.13424/) (2021)
- [Cross-conditioned Diffusion Model for Medical Image to Image Translation](https://mlsanity.com/paper/2409.08500/) (2024)
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- [MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models](https://mlsanity.com/paper/2405.14338/) (2024)
- [Decoder-Only Image Registration](https://mlsanity.com/paper/2402.03585/) (2024)
- [Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting](https://mlsanity.com/paper/2203.00157/) (2022)
- [Dual-Branch Squeeze-Fusion-Excitation Module for Cross-Modality Registration of Cardiac SPECT and CT](https://mlsanity.com/paper/2206.05278/) (2022)
- [SAMReg: SAM-enabled Image Registration with ROI-based Correspondence](https://mlsanity.com/paper/2410.14083/) (2024)
- [FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration](https://mlsanity.com/paper/2407.11299/) (2024)
- [Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2408.03616/) (2024)
- [RS-MOCO: A deep learning-based topology-preserving image registration method for cardiac T1 mapping](https://mlsanity.com/paper/2410.11651/) (2024)
- [EAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation](https://mlsanity.com/paper/2501.01658/) (2025)
- [Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems](https://mlsanity.com/paper/2606.10431/) (2026)
- [Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping](https://mlsanity.com/paper/2307.11989/) (2023)
- [Learning Semi-Supervised Medical Image Segmentation from Spatial Registration](https://mlsanity.com/paper/2409.10422/) (2024)
- [EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration](https://mlsanity.com/paper/2411.15271/) (2024)
- [PathoSyn: Imaging-Pathology MRI Synthesis via Disentangled Deviation Diffusion](https://mlsanity.com/paper/2512.23130/) (2025)
- [GATS: Gaussian Aware Temporal Scaling Transformer for Invariant 4D Spatio-Temporal Point Cloud Representation](https://mlsanity.com/paper/2603.16154/) (2026)

## Abstract (arXiv)

Medical image segmentation is a relevant task as it serves as the first step for several diagnosis processes, thus it is indispensable in clinical usage. Whilst major success has been reported using supervised techniques, they assume a large and well-representative labelled set. This is a strong assumption in the medical domain where annotations are expensive, time-consuming, and inherent to human bias. To address this problem, unsupervised techniques have been proposed in the literature yet it is still an open problem due to the difficulty of learning any transformation pattern. In this work, we present a novel optimisation model framed into a new CNN-based contrastive registration architecture for unsupervised medical image segmentation. The core of our approach is to exploit image-level registration and feature-level from a contrastive learning mechanism, to perform registration-based segmentation. Firstly, we propose an architecture to capture the image-to-image transformation pattern via registration for unsupervised medical image segmentation. Secondly, we embed a contrastive learning mechanism into the registration architecture to enhance the discriminating capacity of the network in the feature-level. We show that our proposed technique mitigates the major drawbacks of existing unsupervised techniques. We demonstrate, through numerical and visual experiments, that our technique substantially outperforms the current state-of-the-art unsupervised segmentation methods on two major medical image datasets.