FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis

• URL: http://arxiv.org/abs/2412.06690v2
• Date: Tue, 06 May 2025 07:21:59 GMT
• Title: FedSynthCT-Brain: A Federated Learning Framework for Multi-Institutional Brain MRI-to-CT Synthesis
• Authors: Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien, Oliver Blanck, Francesco Cicone, Giuseppe Lucio Cascini, Paolo Zaffino, Lucia Migliorelli, Maria Francesca Spadea,
• Abstract summary: Fed SynthCT-Brain is an approach based on the Federated Learning paradigm for MRI-to-sCT in brain imaging.<n>Cross-silo horizontal FL approach allows multiple centres to collaboratively train a U-Net-based deep learning model.
• Score: 1.7239326521335454
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Deep learning methods for MRI-to-sCT have shown promising results, but their reliance on single-centre training dataset limits generalisation capabilities to diverse clinical settings. Moreover, creating centralised multi-centre datasets may pose privacy concerns. To address the aforementioned issues, we introduced FedSynthCT-Brain, an approach based on the Federated Learning (FL) paradigm for MRI-to-sCT in brain imaging. This is among the first applications of FL for MRI-to-sCT, employing a cross-silo horizontal FL approach that allows multiple centres to collaboratively train a U-Net-based deep learning model. We validated our method using real multicentre data from four European and American centres, simulating heterogeneous scanner types and acquisition modalities, and tested its performance on an independent dataset from a centre outside the federation. In the case of the unseen centre, the federated model achieved a median Mean Absolute Error (MAE) of $102.0$ HU across 23 patients, with an interquartile range of $96.7-110.5$ HU. The median (interquartile range) for the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PNSR) were $0.89 (0.86-0.89)$ and $26.58 (25.52-27.42)$, respectively. The analysis of the results showed acceptable performances of the federated approach, thus highlighting the potential of FL to enhance MRI-to-sCT to improve generalisability and advancing safe and equitable clinical applications while fostering collaboration and preserving data privacy.

Related papers

• NeuroMoE: A Transformer-Based Mixture-of-Experts Framework for Multi-Modal Neurological Disorder Classification [3.5313393560458826]
  Deep Learning has recently emerged as a powerful tool for extracting meaningful patterns from medical data to aid in diagnosis.<n>We propose a novel transformer-based Mixture-of-Experts (MoE) framework for classifying neurological disorders.<n>Our framework achieves a validation accuracy of 82.47%, outperforming baseline methods by over 10%.
  arXiv  Detail & Related papers  (2025-06-17T20:40:06Z)
• Towards a general-purpose foundation model for fMRI analysis [58.06455456423138]
  We introduce NeuroSTORM, a framework that learns from 4D fMRI volumes and enables efficient knowledge transfer across diverse applications.<n>NeuroSTORM is pre-trained on 28.65 million fMRI frames (>9,000 hours) from over 50,000 subjects across multiple centers and ages 5 to 100.<n>It outperforms existing methods across five tasks: age/gender prediction, phenotype prediction, disease diagnosis, fMRI-to-image retrieval, and task-based fMRI.
  arXiv  Detail & Related papers  (2025-06-11T23:51:01Z)
• A Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck [0.0]
  Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT)<n>We propose a cross-silo horizontal federated learning (FL) approach for CBCT-to-sCT synthesis in the head and neck region.
  arXiv  Detail & Related papers  (2025-06-10T10:10:56Z)
• Synthetic CT image generation from CBCT: A Systematic Review [44.01505745127782]
  Generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT.
  arXiv  Detail & Related papers  (2025-01-22T13:54:07Z)
• DDEvENet: Evidence-based Ensemble Learning for Uncertainty-aware Brain Parcellation Using Diffusion MRI [5.757390718589337]
  EVENet is an Evidence-based Ensemble Neural Network for anatomical brain parcellation using diffusion MRI. We obtained accurate parcellation and uncertainty estimates across different datasets from healthy and clinical populations. Thanks to the uncertainty estimation, our EVENet approach demonstrates a good ability to detect abnormal brain regions in patients with lesions.
  arXiv  Detail & Related papers  (2024-09-11T05:26:23Z)
• Fed-MUnet: Multi-modal Federated Unet for Brain Tumor Segmentation [8.757069870788525]
  We propose a novel multi-modal FL framework for brain tumor segmentation (Fed-MUnet) that is suitable for FL training. We evaluate our approach with the BraTS2022 datasets, which are publicly available.
  arXiv  Detail & Related papers  (2024-09-02T07:55:52Z)
• CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI [39.0162369912624]
  The CMRxRecon2024 dataset is the largest and most diverse publicly available cardiac k-space dataset. It is acquired from 330 healthy volunteers, covering commonly used modalities, anatomical views, and acquisition trajectories in clinical cardiac MRI.
  arXiv  Detail & Related papers  (2024-06-27T09:50:20Z)
• Deep learning-based brain segmentation model performance validation with clinical radiotherapy CT [0.0]
  This study validates the SynthSeg robust brain segmentation model on computed tomography (CT) Brain segmentations from CT and MRI were obtained with SynthSeg model, a component of the Freesurfer imaging suite. CT performance is lower than MRI based on the integrated QC scores, but low-quality segmentations can be excluded with QC-based thresholding.
  arXiv  Detail & Related papers  (2024-06-25T09:56:30Z)
• Empowering Healthcare through Privacy-Preserving MRI Analysis [3.6394715554048234]
  We introduce the Ensemble-Based Federated Learning (EBFL) Framework. EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. We have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances.
  arXiv  Detail & Related papers  (2024-03-14T19:51:18Z)
• Cycle-consistent Generative Adversarial Network Synthetic CT for MR-only Adaptive Radiation Therapy on MR-Linac [0.0]
  Cycle-GAN model was trained with MRI and CT scan slices from MR-LINAC treatments, generating sCT volumes. Dosimetric evaluations indicated minimal differences between sCTs and dCTs, with sCTs showing better air-bubble reconstruction.
  arXiv  Detail & Related papers  (2023-12-03T04:38:17Z)
• CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [62.61209705638161]
  There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
  arXiv  Detail & Related papers  (2023-09-19T15:14:42Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification [42.75911994044675]
  We present a novel approach for unpaired image-to-image translation of prostate MRIs and an uncertainty-aware training approach for classifying clinically significant PCa. Our approach involves a novel pipeline for translating unpaired 3.0T multi-parametric prostate MRIs to 1.5T, thereby augmenting the available training data. Our experiments demonstrate that the proposed method significantly improves the Area Under ROC Curve (AUC) by over 20% compared to the previous work.
  arXiv  Detail & Related papers  (2023-07-02T05:26:54Z)
• Z-SSMNet: Zonal-aware Self-supervised Mesh Network for Prostate Cancer Detection and Diagnosis with Bi-parametric MRI [14.101371684361675]
  We propose a Zonal-aware Self-supervised Mesh Network (Z-SSMNet) Z-SSMNet adaptively integrates multi-dimensional (2D/2.5D/3D) convolutions to learn dense intra-slice information and sparse inter-slice information of the anisotropic bpMRI. A self-supervised learning (SSL) technique is proposed to pre-train our network using large-scale unlabeled data.
  arXiv  Detail & Related papers  (2022-12-12T10:08:46Z)
• Complex-valued Federated Learning with Differential Privacy and MRI Applications [51.34714485616763]
  We introduce the complex-valued Gaussian mechanism, whose behaviour we characterise in terms of $f$-DP, $(varepsilon, delta)$-DP and R'enyi-DP. We present novel complex-valued neural network primitives compatible with DP. Experimentally, we showcase a proof-of-concept by training federated complex-valued neural networks with DP on a real-world task.
  arXiv  Detail & Related papers  (2021-10-07T14:03:00Z)
• Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification [0.6117371161379209]
  We have developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40%. Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.
  arXiv  Detail & Related papers  (2021-06-14T12:19:27Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

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.