Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients

• URL: http://arxiv.org/abs/2211.11025v1
• Date: Sun, 20 Nov 2022 17:02:52 GMT
• Title: Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients
• Authors: Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert
• Abstract summary: This paper describes our contribution to the Registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022) We developed an enhanced unsupervised learning-based method that extends the iRegNet. Experimental findings show that the enhanced self-supervised model is able to improve the initial mean median registration absolute error (MAE) from 8.20 (7.62) mm to the lowest value of 3.51 (3.50) mm.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This paper describes our contribution to the Registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends the iRegNet. In particular, incorporating an unsupervised learning-based paradigm as well as several minor modifications to the network pipeline, allows the enhanced iRegNet method to achieve respectable results. Experimental findings show that the enhanced self-supervised model is able to improve the initial mean median registration absolute error (MAE) from 8.20 (7.62) mm to the lowest value of 3.51 (3.50) for the training set while achieving an MAE of 2.93 (1.63) mm for the validation set. Additional qualitative validation of this study was conducted through overlaying pre-post MRI pairs before and after the de-formable registration. The proposed method scored 5th place during the testing phase of the MICCAI BraTS-Reg 2022 challenge. The docker image to reproduce our BraTS-Reg submission results will be publicly available.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models [76.43625653814911]
  Diffusion models have gained popularity for accelerated MRI reconstruction due to their high sample quality. They can effectively serve as rich data priors while incorporating the forward model flexibly at inference time. We introduce SURE-based MRI Reconstruction with Diffusion models (SMRD) to enhance robustness during testing.
  arXiv  Detail & Related papers  (2023-10-03T05:05:35Z)
• 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)
• 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)
• 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)
• Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision [11.795108660250843]
  We focus on brain tumor sequence registration between pre-operative and follow-up MRI scans of brain glioma patients. Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. We adopt our recently proposed Non-Iterative Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method for coarse-to-fine registering images with large deformations.
  arXiv  Detail & Related papers  (2022-11-15T03:58:47Z)
• 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)
• Cross-Modality Image Registration using a Training-Time Privileged Third Modality [5.78335050301421]
  We propose a learning from privileged modality algorithm to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients.
  arXiv  Detail & Related papers  (2022-07-26T13:50:30Z)
• A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction [50.1787181309337]
  We propose a convolutional long short-term memory (Conv-LSTM) based recurrent neural network (RNN), or ConvLR, to reconstruct interventional images with golden-angle radial sampling. The proposed algorithm has the potential to achieve real-time i-MRI for DBS and can be used for general purpose MR-guided intervention.
  arXiv  Detail & Related papers  (2022-03-28T14:03:45Z)
• The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients [31.567542945171834]
  We describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge. BraTS-Reg is the first public benchmark environment for deformable registration algorithms. The aim of BraTS-Reg is to continue to serve as an active resource for research.
  arXiv  Detail & Related papers  (2021-12-13T19:25:16Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.