Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans

• URL: http://arxiv.org/abs/2206.03900v1
• Date: Wed, 8 Jun 2022 13:53:03 GMT
• Title: Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans
• Authors: Tony C. W. Mok, Albert C. S. Chung
• Abstract summary: We propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment.
• Score: 11.4219428942199
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Registration of pre-operative and post-recurrence brain images is often needed to evaluate the effectiveness of brain gliomas treatment. While recent deep learning-based deformable registration methods have achieved remarkable success with healthy brain images, most of them would be unable to accurately align images with pathologies due to the absent correspondences in the reference image. In this paper, we propose a deep learning-based deformable registration method that jointly estimates regions with absent correspondence and bidirectional deformation fields. A forward-backward consistency constraint is used to aid in the localization of the resection and recurrence region from voxels with absence correspondences in the two images. Results on 3D clinical data from the BraTS-Reg challenge demonstrate our method can improve image alignment compared to traditional and deep learning-based registration approaches with or without cost function masking strategy. The source code is available at https://github.com/cwmok/DIRAC.

Related papers

• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration [13.551672729289265]
  We propose GIRNet, a novel unsupervised approach for pathological image registration. The registration of pathological images is achieved in a completely unsupervised learning framework. Our results show that our method can accurately achieve the registration of pathological images and identify lesions even in challenging imaging modalities.
  arXiv  Detail & Related papers  (2023-10-17T07:13:28Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Joint segmentation and discontinuity-preserving deformable registration: Application to cardiac cine-MR images [74.99415008543276]
  Most deep learning-based registration methods assume that the deformation fields are smooth and continuous everywhere in the image domain. We propose a novel discontinuity-preserving image registration method to tackle this challenge, which ensures globally discontinuous and locally smooth deformation fields. A co-attention block is proposed in the segmentation component of the network to learn the structural correlations in the input images. We evaluate our method on the task of intra-subject-temporal image registration using large-scale cinematic cardiac magnetic resonance image sequences.
  arXiv  Detail & Related papers  (2022-11-24T23:45:01Z)
• Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients [11.4219428942199]
  We propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans. Our method achieves a median absolute error of 1.64 mm and 88% of successful registration rate in the validation set of BraTS-Reg challenge.
  arXiv  Detail & Related papers  (2022-10-20T06:37:40Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• A Deep Discontinuity-Preserving Image Registration Network [73.03885837923599]
  Most deep learning-based registration methods assume that the desired deformation fields are globally smooth and continuous. We propose a weakly-supervised Deep Discontinuity-preserving Image Registration network (DDIR) to obtain better registration performance and realistic deformation fields. We demonstrate that our method achieves significant improvements in registration accuracy and predicts more realistic deformations, in registration experiments on cardiac magnetic resonance (MR) images.
  arXiv  Detail & Related papers  (2021-07-09T13:35:59Z)
• Representing Ambiguity in Registration Problems with Conditional Invertible Neural Networks [28.81229531636232]
  In this paper, we explore the application of invertible neural networks (INNs) as core component of a registration methodology. In a first feasibility study, we test the approach for a 2D 3D registration setting by registering spinal CT volumes to X-ray images.
  arXiv  Detail & Related papers  (2020-12-15T10:28:41Z)
• F3RNet: Full-Resolution Residual Registration Network for Deformable Image Registration [21.99118499516863]
  Deformable image registration (DIR) is essential for many image-guided therapies. We propose a novel unsupervised registration network, namely the Full-Resolution Residual Registration Network (F3RNet) One stream takes advantage of the full-resolution information that facilitates accurate voxel-level registration. The other stream learns the deep multi-scale residual representations to obtain robust recognition.
  arXiv  Detail & Related papers  (2020-09-15T15:05:54Z)
• An Auto-Context Deformable Registration Network for Infant Brain MRI [54.57017031561516]
  We propose an infant-dedicated deep registration network that uses the auto-context strategy to gradually refine the deformation fields. Our method estimates the deformation fields by invoking a single network multiple times for iterative deformation refinement. Experimental results in comparison with state-of-the-art registration methods indicate that our method achieves higher accuracy while at the same time preserves the smoothness of the deformation fields.
  arXiv  Detail & Related papers  (2020-05-19T06:00:13Z)
• A Multiple Decoder CNN for Inverse Consistent 3D Image Registration [18.017296651822857]
  Deep learning technologies have drastically decreased the registration time and increased registration accuracy. We propose a registration framework with inverse consistency. We perform training and testing of the method on the publicly available LPBA40 MRI dataset and demonstrate strong performance.
  arXiv  Detail & Related papers  (2020-02-15T23:23:09Z)

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.