# LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2003.07072 · HTML: https://mlsanity.com/paper/2003.07072/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [One-Prompt to Segment All Medical Images](https://mlsanity.com/paper/2305.10300/) (2023)
- [Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training](https://mlsanity.com/paper/2303.00874/) (2023)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation](https://mlsanity.com/paper/2102.02033/) (2021)
- [A Unified Framework for Generalized Low-Shot Medical Image Segmentation with Scarce Data](https://mlsanity.com/paper/2110.09260/) (2021)
- [Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation](https://mlsanity.com/paper/2102.10935/) (2021)
- [Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2110.02117/) (2021)
- [One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs](https://mlsanity.com/paper/2201.11000/) (2022)
- [PC-SwinMorph: Patch Representation for Unsupervised Medical Image Registration and Segmentation](https://mlsanity.com/paper/2203.05684/) (2022)
- [CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation](https://mlsanity.com/paper/2209.11359/) (2022)
- [Warp-Refine Propagation: Semi-Supervised Auto-labeling via Cycle-consistency](https://mlsanity.com/paper/2109.13432/) (2021)
- [Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation](https://mlsanity.com/paper/2103.16344/) (2021)
- [Towards Single Stage Weakly Supervised Semantic Segmentation](https://mlsanity.com/paper/2106.10309/) (2021)
- [DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images](https://mlsanity.com/paper/2410.03058/) (2024)
- [MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network](https://mlsanity.com/paper/2111.12123/) (2021)
- [Morphology-Aware Interactive Keypoint Estimation](https://mlsanity.com/paper/2209.07163/) (2022)
- [Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning](https://mlsanity.com/paper/2502.05282/) (2025)
- [Robust One-shot Segmentation of Brain Tissues via Image-aligned Style Transformation](https://mlsanity.com/paper/2211.14521/) (2022)
- [One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data](https://mlsanity.com/paper/2307.15198/) (2023)
- [Deep learning based domain adaptation for mitochondria segmentation on EM volumes](https://mlsanity.com/paper/2202.10773/) (2022)
- [Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency](https://mlsanity.com/paper/2406.10175/) (2024)
- [Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2408.03616/) (2024)
- [Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation](https://mlsanity.com/paper/2211.08146/) (2022)
- [AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2309.00971/) (2023)
- [End-to-End Deep Learning for Structural Brain Imaging: A Unified Framework](https://mlsanity.com/paper/2502.18523/) (2025)
- [Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation](https://mlsanity.com/paper/2510.13075/) (2025)
- [Uni-Encoder Meets Multi-Encoders: Representation Before Fusion for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2604.22177/) (2026)

## Abstract (arXiv)

We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-based segmentation problem, where voxel-wise correspondence from the atlas to the unlabelled data is learned. Subsequently, segmentation label of the atlas can be transferred to the unlabelled data with the learned correspondence. However, since ground truth correspondence between images is usually unavailable, the learning system must be well-supervised to avoid mode collapse and convergence failure. To overcome this difficulty, we resort to the forward-backward consistency, which is widely used in correspondence problems, and additionally learn the backward correspondences from the warped atlases back to the original atlas. This cycle-correspondence learning design enables a variety of extra, cycle-consistency-based supervision signals to make the training process stable, while also boost the performance. We demonstrate the superiority of our method over both deep learning-based one-shot segmentation methods and a classical multi-atlas segmentation method via thorough experiments.