TCIP: Threshold-Controlled Iterative Pyramid Network for Deformable Medical Image Registration

• URL: http://arxiv.org/abs/2510.07666v1
• Date: Thu, 09 Oct 2025 01:38:40 GMT
• Title: TCIP: Threshold-Controlled Iterative Pyramid Network for Deformable Medical Image Registration
• Authors: Heming Wu, Di Wang, Tai Ma, Peng Zhao, Yubin Xiao, Zhongke Wu, Xing-Ce Wang, Chuang Li, Xuan Wu, You Zhou,
• Abstract summary: We propose the Feature-Enhanced Residual Module (FERM) as the core component of each decoding layer in the pyramid network.<n>FERM comprises three sequential blocks that extract anatomical semantic features, learn to suppress irrelevant features, and estimate the final deformation field.<n>We coin the model that integrates FERM and TCI as Threshold-Controlled Iterative Pyramid (TCIP)
• Score: 21.283219565079413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although pyramid networks have demonstrated superior performance in deformable medical image registration, their decoder architectures are inherently prone to propagating and accumulating anatomical structure misalignments. Moreover, most existing models do not adaptively determine the number of iterations for optimization under varying deformation requirements across images, resulting in either premature termination or excessive iterations that degrades registration accuracy. To effectively mitigate the accumulation of anatomical misalignments, we propose the Feature-Enhanced Residual Module (FERM) as the core component of each decoding layer in the pyramid network. FERM comprises three sequential blocks that extract anatomical semantic features, learn to suppress irrelevant features, and estimate the final deformation field, respectively. To adaptively determine the number of iterations for varying images, we propose the dual-stage Threshold-Controlled Iterative (TCI) strategy. In the first stage, TCI assesses registration stability and with asserted stability, it continues with the second stage to evaluate convergence. We coin the model that integrates FERM and TCI as Threshold-Controlled Iterative Pyramid (TCIP). Extensive experiments on three public brain MRI datasets and one abdomen CT dataset demonstrate that TCIP outperforms the state-of-the-art (SOTA) registration networks in terms of accuracy, while maintaining comparable inference speed and a compact model parameter size. Finally, we assess the generalizability of FERM and TCI by integrating them with existing registration networks and further conduct ablation studies to validate the effectiveness of these two proposed methods.

Related papers

• PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT [8.358409792893278]
  We develop a domain-adaptive transformer segmentation framework tailored for multi-institutional Quantitative computed tomography (QCT)<n>Our model is trained and validated on one of the largest hip fracture related research cohorts to date, comprising 1,024 QCT images scans from Tulane University and 384 scans from Rochester, Minnesota for proximal femur segmentation.
  arXiv  Detail & Related papers  (2025-10-30T18:07:56Z)
• Why Registration Quality Matters: Enhancing sCT Synthesis with IMPACT-Based Registration [1.2560645967579729]
  Our model is a 2.5D U-Net++ with a ResNet-34 encoder, trained jointly across anatomical regions and fine-tuned per region.<n>On the local test sets, IMPACT-based registration achieved more accurate and anatomically consistent alignments than mutual-information-based registration.
  arXiv  Detail & Related papers  (2025-10-24T11:40:21Z)
• Deep Learning-Based Cross-Anatomy CT Synthesis Using Adapted nnResU-Net with Anatomical Feature Prioritized Loss [3.583510367091671]
  We present a patch-based 3D nnUNet adaptation for MR to CT and CBCT to CT image translation using the multicenter SynthRAD2025 dataset.
  arXiv  Detail & Related papers  (2025-09-26T14:22:15Z)
• MedSeqFT: Sequential Fine-tuning Foundation Models for 3D Medical Image Segmentation [55.37355146924576]
  MedSeqFT is a sequential fine-tuning framework for medical image analysis.<n>It adapts pre-trained models to new tasks while refining their representational capacity.<n>It consistently outperforms state-of-the-art fine-tuning strategies.
  arXiv  Detail & Related papers  (2025-09-07T15:22:53Z)
• Clinical Metadata Guided Limited-Angle CT Image Reconstruction [8.987752589028348]
  Limited-angle computed tomography (LACT) offers improved temporal resolution and reduced radiation dose for cardiac imaging, but suffers from severe artifacts due to truncated projections.<n>To address the ill-posedness of LACT reconstruction, we propose a two-stage diffusion framework guided by structured clinical metadata.
  arXiv  Detail & Related papers  (2025-09-01T20:14:15Z)
• IMPACT: A Generic Semantic Loss for Multimodal Medical Image Registration [0.46904601975060667]
  IMPACT (Image Metric with Pretrained model-Agnostic Comparison for Transmodality registration) is a novel similarity metric designed for robust multimodal image registration.<n>It defines a semantic similarity measure based on the comparison of deep features extracted from large-scale pretrained segmentation models.<n>It was evaluated on five challenging 3D registration tasks involving thoracic CT/CBCT and pelvic MR/CT datasets.
  arXiv  Detail & Related papers  (2025-03-31T14:08:21Z)
• SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging [59.78761085714715]
  This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework for liver lesion classification. The proposed framework has been validated through comprehensive experiments on two clinical datasets. To support the scientific community, we are releasing our extensive multi-phase MR dataset for liver lesion analysis to the public.
  arXiv  Detail & Related papers  (2024-02-27T06:32:56Z)
• GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration [62.41725951450803]
  Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the field. We construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyper parameter-free balance on multiple losses. Our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference.
  arXiv  Detail & Related papers  (2023-06-26T13:32:09Z)
• 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors [1.2234742322758418]
  We propose a two-stage cascaded network based on the Inception and TransMorph models. Loss function was composed of a standard image similarity measure, a diffusion regularizer, and an edge-map similarity measure added to overcome intensity dependence. We achieved 6th place at the time of model submission in the final testing phase of the BraTS-Reg challenge.
  arXiv  Detail & Related papers  (2022-12-08T22:00:07Z)
• InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal Artifact Reduction in CT Images [53.4351366246531]
  We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
  arXiv  Detail & Related papers  (2021-12-23T15:52:37Z)
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation [56.622832383316215]
  We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our empirical results show that CyTran outperforms all competing methods.
  arXiv  Detail & Related papers  (2021-10-12T23:25:03Z)
• Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation [64.59521853145368]
  Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features.
  arXiv  Detail & Related papers  (2021-09-08T07:56:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.