# CDIDN: A Registration Model with High Deformation Impedance Capability for Long-Term Tracking of Pulmonary Lesion Dynamics

arXiv: https://arxiv.org/abs/2305.11024 · HTML: https://mlsanity.com/paper/2305.11024/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 14 of 14)

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Learning Deconvolution Network for Semantic Segmentation](https://mlsanity.com/paper/1505.04366/) (2015)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration](https://mlsanity.com/paper/1807.03361/) (2018)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [Unsupervised 3D End-to-End Medical Image Registration with Volume Tweening Network](https://mlsanity.com/paper/1902.05020/) (2019)
- [One Shot Learning for Deformable Medical Image Registration and Periodic Motion Tracking](https://mlsanity.com/paper/1907.04641/) (2019)
- [3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations](https://mlsanity.com/paper/1908.10235/) (2019)

## Abstract (arXiv)

We study the problem of registration for medical CT images from a novel perspective -- the sensitivity to degree of deformations in CT images. Although some learning-based methods have shown success in terms of average accuracy, their ability to handle regions with local large deformation (LLD) may significantly decrease compared to dealing with regions with minor deformation. This motivates our research into this issue. Two main causes of LLDs are organ motion and changes in tissue structure, with the latter often being a long-term process. In this paper, we propose a novel registration model called Cascade-Dilation Inter-Layer Differential Network (CDIDN), which exhibits both high deformation impedance capability (DIC) and accuracy. CDIDN improves its resilience to LLDs in CT images by enhancing LLDs in the displacement field (DF). It uses a feature-based progressive decomposition of LLDs, blending feature flows of different levels into a main flow in a top-down manner. It leverages Inter-Layer Differential Module (IDM) at each level to locally refine the main flow and globally smooth the feature flow, and also integrates feature velocity fields that can effectively handle feature deformations of various degrees. We assess CDIDN using lungs as representative organs with large deformation. Our findings show that IDM significantly enhances LLDs of the DF, by which improves the DIC and accuracy of the model. Compared with other outstanding learning-based methods, CDIDN exhibits the best DIC and excellent accuracy. Based on vessel enhancement and enhanced LLDs of the DF, we propose a novel method to accurately track the appearance, disappearance, enlargement, and shrinkage of pulmonary lesions, which effectively addresses detection of early lesions and peripheral lung lesions, issues of false enlargement, false shrinkage, and mutilation of lesions.