# SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid

arXiv: https://arxiv.org/abs/2307.09727 · HTML: https://mlsanity.com/paper/2307.09727/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 19 Jul 2023.

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

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images](https://mlsanity.com/paper/2012.02383/) (2020)
- [Accurate Optical Flow via Direct Cost Volume Processing](https://mlsanity.com/paper/1704.07325/) (2017)
- [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019)
- [textttGradICON: Approximate Diffeomorphisms via Gradient Inverse Consistency](https://mlsanity.com/paper/2206.05897/) (2022)
- [Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks](https://mlsanity.com/paper/1907.10931/) (2019)
- [Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021](https://mlsanity.com/paper/2112.03053/) (2021)
- [Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids](https://mlsanity.com/paper/1604.03513/) (2016)
- [SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings](https://mlsanity.com/paper/2109.11572/) (2021)
- [Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond](https://mlsanity.com/paper/2004.14557/) (2020)
- [Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions](https://mlsanity.com/paper/2203.06810/) (2022)

## Built on by (most cited first, 14 of 14)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Spatially Covariant Image Registration with Text Prompts](https://mlsanity.com/paper/2311.15607/) (2023)
- [Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration](https://mlsanity.com/paper/2402.18933/) (2024)
- [multiGradICON: A Foundation Model for Multimodal Medical Image Registration](https://mlsanity.com/paper/2408.00221/) (2024)
- [Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging](https://mlsanity.com/paper/2311.16914/) (2023)
- [General Purpose Image Encoder DINOv2 for Medical Image Registration](https://mlsanity.com/paper/2402.15687/) (2024)
- [A Modality-agnostic Multi-task Foundation Model for Human Brain Imaging](https://mlsanity.com/paper/2509.00549/) (2025)
- [Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration](https://mlsanity.com/paper/2406.07361/) (2024)
- [EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration](https://mlsanity.com/paper/2403.11026/) (2024)
- [Encoder-Only Image Registration](https://mlsanity.com/paper/2509.00451/) (2025)
- [UniReg: Conditional Unified Model for Medical Image Registration](https://mlsanity.com/paper/2503.12868/) (2025)
- [SAMIR, an efficient registration framework via robust feature learning from SAM](https://mlsanity.com/paper/2509.13629/) (2025)
- [Ideal Registration? Segmentation is All You Need](https://mlsanity.com/paper/2509.15784/) (2025)
- [CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration](https://mlsanity.com/paper/2603.23694/) (2026)

## Abstract (arXiv)

Estimating displacement vector field via a cost volume computed in the feature space has shown great success in image registration, but it suffers excessive computation burdens. Moreover, existing feature descriptors only extract local features incapable of representing the global semantic information, which is especially important for solving large transformations. To address the discussed issues, we propose SAMConvex, a fast coarse-to-fine discrete optimization method for CT registration that includes a decoupled convex optimization procedure to obtain deformation fields based on a self-supervised anatomical embedding (SAM) feature extractor that captures both local and global information. To be specific, SAMConvex extracts per-voxel features and builds 6D correlation volumes based on SAM features, and iteratively updates a flow field by performing lookups on the correlation volumes with a coarse-to-fine scheme. SAMConvex outperforms the state-of-the-art learning-based methods and optimization-based methods over two inter-patient registration datasets (Abdomen CT and HeadNeck CT) and one intra-patient registration dataset (Lung CT). Moreover, as an optimization-based method, SAMConvex only takes ~2s (~5s with instance optimization) for one paired images.