# SAME: Deformable Image Registration based on Self-supervised Anatomical Embeddings

arXiv: https://arxiv.org/abs/2109.11572 · HTML: https://mlsanity.com/paper/2109.11572/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [SAM: Self-supervised Learning of Pixel-wise Anatomical Embeddings in Radiological Images](https://mlsanity.com/paper/2012.02383/) (2020)
- [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019)
- [Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images](https://mlsanity.com/paper/1706.03702/) (2017)
- [DeepStationing: Thoracic Lymph Node Station Parsing in CT Scans using Anatomical Context Encoding and Key Organ Auto-Search](https://mlsanity.com/paper/2109.09271/) (2021)
- [JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans](https://mlsanity.com/paper/2005.12209/) (2020)

## Built on by (most cited first, 21 of 21)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis](https://mlsanity.com/paper/2301.00772/) (2023)
- [A Robust and Interpretable Deep Learning Framework for Multi-modal Registration via Keypoints](https://mlsanity.com/paper/2304.09941/) (2023)
- [Deep Learning in Medical Image Registration: Magic or Mirage?](https://mlsanity.com/paper/2408.05839/) (2024)
- [Spatially Covariant Image Registration with Text Prompts](https://mlsanity.com/paper/2311.15607/) (2023)
- [SAMConvex: Fast Discrete Optimization for CT Registration using Self-supervised Anatomical Embedding and Correlation Pyramid](https://mlsanity.com/paper/2307.09727/) (2023)
- [MambaMorph: a Mamba-based Framework for Medical MR-CT Deformable Registration](https://mlsanity.com/paper/2401.13934/) (2024)
- [SAME++: A Self-supervised Anatomical eMbeddings Enhanced medical image registration framework using stable sampling and regularized transformation](https://mlsanity.com/paper/2311.14986/) (2023)
- [RayEmb: Arbitrary Landmark Detection in X-Ray Images Using Ray Embedding Subspace](https://mlsanity.com/paper/2410.08152/) (2024)
- [IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration](https://mlsanity.com/paper/2503.24121/) (2025)
- [A Hierarchical Descriptor Framework for On-the-Fly Anatomical Location Matching between Longitudinal Studies](https://mlsanity.com/paper/2308.07337/) (2023)
- [EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration](https://mlsanity.com/paper/2403.11026/) (2024)
- [Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images](https://mlsanity.com/paper/2307.03535/) (2023)
- [Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer](https://mlsanity.com/paper/2409.11910/) (2024)
- [UniReg: Conditional Unified Model for Medical Image Registration](https://mlsanity.com/paper/2503.12868/) (2025)
- [Collaborative Quantization Embeddings for Intra-Subject Prostate MR Image Registration](https://mlsanity.com/paper/2207.06189/) (2022)
- [Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration](https://mlsanity.com/paper/2401.02141/) (2024)
- [RealKeyMorph: Keypoints in Real-world Coordinates for Resolution-agnostic Image Registration](https://mlsanity.com/paper/2506.10344/) (2025)
- [A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study](https://mlsanity.com/paper/2601.16467/) (2026)
- [CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration](https://mlsanity.com/paper/2603.23694/) (2026)
- [Benchmarking Intra-Patient 3D Deformable Multimodal Image Registration](https://mlsanity.com/paper/2609.15669/) (2026)

## Abstract (arXiv)

In this work, we introduce a fast and accurate method for unsupervised 3D medical image registration. This work is built on top of a recent algorithm SAM, which is capable of computing dense anatomical/semantic correspondences between two images at the pixel level. Our method is named SAME, which breaks down image registration into three steps: affine transformation, coarse deformation, and deep deformable registration. Using SAM embeddings, we enhance these steps by finding more coherent correspondences, and providing features and a loss function with better semantic guidance. We collect a multi-phase chest computed tomography dataset with 35 annotated organs for each patient and conduct inter-subject registration for quantitative evaluation. Results show that SAME outperforms widely-used traditional registration techniques (Elastix FFD, ANTs SyN) and learning based VoxelMorph method by at least 4.7% and 2.7% in Dice scores for two separate tasks of within-contrast-phase and across-contrast-phase registration, respectively. SAME achieves the comparable performance to the best traditional registration method, DEEDS (from our evaluation), while being orders of magnitude faster (from 45 seconds to 1.2 seconds).