Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation

• URL: http://arxiv.org/abs/2211.14282v1
• Date: Fri, 25 Nov 2022 18:29:53 GMT
• Title: Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation
• Authors: Priscille de Dumast, Meritxell Bach Cuadra
• Abstract summary: We propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject. Overall, the latter significantly improves the generalization of segmentation methods over SR pipelines.
• Score: 0.348097307252416
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Quantitative analysis of in utero human brain development is crucial for abnormal characterization. Magnetic resonance image (MRI) segmentation is therefore an asset for quantitative analysis. However, the development of automated segmentation methods is hampered by the scarce availability of fetal brain MRI annotated datasets and the limited variability within these cohorts. In this context, we propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject with different parameters, thus as an efficient tuning-free data augmentation strategy. Overall, the latter significantly improves the generalization of segmentation methods over SR pipelines.

Related papers

• MindFormer: A Transformer Architecture for Multi-Subject Brain Decoding via fMRI [50.55024115943266]
  We introduce a new Transformer architecture called MindFormer to generate fMRI-conditioned feature vectors. MindFormer incorporates two key innovations: 1) a novel training strategy based on the IP-Adapter to extract semantically meaningful features from fMRI signals, and 2) a subject specific token and linear layer that effectively capture individual differences in fMRI signals.
  arXiv  Detail & Related papers  (2024-05-28T00:36:25Z)
• Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data [1.1936126505067601]
  We introduce Fetal SynthSeg, a domain randomization method to segment fetal brain MRI. Our results show that models trained solely on synthetic data outperform models trained on real data. We extend our evaluation to 40 subjects acquired using lowfield (0.55T) MRI and reconstructed with novel SR models.
  arXiv  Detail & Related papers  (2024-03-22T10:42:25Z)
• Anatomically Constrained Tractography of the Fetal Brain [6.112565873653592]
  We advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve tractography results.
  arXiv  Detail & Related papers  (2024-03-04T19:56:19Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• fMRI-PTE: A Large-scale fMRI Pretrained Transformer Encoder for Multi-Subject Brain Activity Decoding [54.17776744076334]
  We propose fMRI-PTE, an innovative auto-encoder approach for fMRI pre-training. Our approach involves transforming fMRI signals into unified 2D representations, ensuring consistency in dimensions and preserving brain activity patterns. Our contributions encompass introducing fMRI-PTE, innovative data transformation, efficient training, a novel learning strategy, and the universal applicability of our approach.
  arXiv  Detail & Related papers  (2023-11-01T07:24:22Z)
• Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction [3.083408283778178]
  This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors.
  arXiv  Detail & Related papers  (2023-09-02T01:33:50Z)
• 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)
• Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities [2.867517731896504]
  Accurately segmenting brain tumor from MR images is the key to clinical diagnostics and treatment planning. It's common to miss some imaging modalities in clinical practice. We present a novel brain tumor segmentation algorithm with missing modalities.
  arXiv  Detail & Related papers  (2021-04-13T14:21:09Z)
• Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset [81.02949933048332]
  This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of Representational Similarity Analysis (RSA) DRSL is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects.
  arXiv  Detail & Related papers  (2020-09-28T18:30:14Z)
• Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness [5.474237208776356]
  The performance of compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts. We propose a new deep learning-based CS-MRI reconstruction method to fully utilise the relationship among sequential MRI slices by coupling Wasserstein Generative Adversarial Networks (WGAN) with Recurrent Neural Networks.
  arXiv  Detail & Related papers  (2020-06-23T11:50:21Z)
• MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data [75.73881040581767]
  We propose a novel multi-site network (MS-Net) for improving prostate segmentation by learning robust representations. Our MS-Net improves the performance across all datasets consistently, and outperforms state-of-the-art methods for multi-site learning.
  arXiv  Detail & Related papers  (2020-02-09T14:11:50Z)

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.