# Deep Complementary Joint Model for Complex Scene Registration and Few-shot Segmentation on Medical Images

arXiv: https://arxiv.org/abs/2008.00710 · HTML: https://mlsanity.com/paper/2008.00710/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 17 of 17)

- [XMorpher: Full Transformer for Deformable Medical Image Registration via Cross Attention](https://mlsanity.com/paper/2206.07349/) (2022)
- [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)
- [Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registration](https://mlsanity.com/paper/2209.05160/) (2022)
- [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)
- [AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration](https://mlsanity.com/paper/2309.05271/) (2023)
- [Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT](https://mlsanity.com/paper/2210.14297/) (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)
- [Unsupervised learning of spatially varying regularization for diffeomorphic image registration](https://mlsanity.com/paper/2412.17982/) (2024)
- [Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2408.03616/) (2024)
- [Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint](https://mlsanity.com/paper/2112.02249/) (2021)
- [AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2309.00971/) (2023)
- [Dual-Attention Frequency Fusion at Multi-Scale for Joint Segmentation and Deformable Medical Image Registration](https://mlsanity.com/paper/2409.19658/) (2024)
- [Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation](https://mlsanity.com/paper/2510.13075/) (2025)

## Abstract (arXiv)

Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoption of the joint models are hindered: 1) the diversity of augmentation data is reduced limiting the further enhancement of segmentation, 2) misaligned regions in weakly supervised data disturb the training process, 3) lack of label-based region constraints in few-shot situation limits the registration performance. We propose a novel Deep Complementary Joint Model (DeepRS) for complex scene registration and few-shot segmentation. We embed a perturbation factor in the registration to increase the activity of deformation thus maintaining the augmentation data diversity. We take a pixel-wise discriminator to extract alignment confidence maps which highlight aligned regions in weakly supervised data so the misaligned regions' disturbance will be suppressed via weighting. The outputs from segmentation model are utilized to implement deep-based region constraints thus relieving the label requirements and bringing fine registration. Extensive experiments on the CT dataset of MM-WHS 2017 Challenge show great advantages of our DeepRS that outperforms the existing state-of-the-art models.