Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains

• URL: http://arxiv.org/abs/2409.01195v1
• Date: Mon, 2 Sep 2024 12:05:03 GMT
• Title: Ground-truth effects in learning-based fiber orientation distribution estimation in neonatal brains
• Authors: Rizhong Lin, Hamza Kebiri, Ali Gholipour, Yufei Chen, Jean-Philippe Thiran, Davood Karimi, Meritxell Bach Cuadra,
• Abstract summary: We train a state-of-the-art model based on the U-Net architecture on both MSMT-CSD and single-shell three-tissue constrained spherical deconvolution (SS3T-CSD) Our results suggest that SS3T-CSD might be more suited for neonatal brains, given that the ratio between single and multiple fiber-estimated voxels with SS3T-CSD is more realistic compared to MSMT-CSD. In an age domain-shift setting, SS3T-CSD maintains robust performance across age groups, indicating its potential for more accurate neonatal brain imaging.
• Score: 8.545738983975639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FOD estimation with deep neural networks has seen growing success, in particular, those of neonates estimated with fewer diffusion measurements. These methods are mostly trained on target FODs reconstructed with multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD), which might not be the ideal ground truth for developing brains. Here, we investigate this hypothesis by training a state-of-the-art model based on the U-Net architecture on both MSMT-CSD and single-shell three-tissue constrained spherical deconvolution (SS3T-CSD). Our results suggest that SS3T-CSD might be more suited for neonatal brains, given that the ratio between single and multiple fiber-estimated voxels with SS3T-CSD is more realistic compared to MSMT-CSD. Additionally, increasing the number of input gradient directions significantly improves performance with SS3T-CSD over MSMT-CSD. Finally, in an age domain-shift setting, SS3T-CSD maintains robust performance across age groups, indicating its potential for more accurate neonatal brain imaging.

Related papers

• Enhancing Angular Resolution via Directionality Encoding and Geometric Constraints in Brain Diffusion Tensor Imaging [70.66500060987312]
  Diffusion-weighted imaging (DWI) is a type of Magnetic Resonance Imaging (MRI) technique sensitised to the diffusivity of water molecules. This work proposes DirGeo-DTI, a deep learning-based method to estimate reliable DTI metrics even from a set of DWIs acquired with the minimum theoretical number (6) of gradient directions.
  arXiv  Detail & Related papers  (2024-09-11T11:12:26Z)
• Self-Supervised Pretext Tasks for Alzheimer's Disease Classification using 3D Convolutional Neural Networks on Large-Scale Synthetic Neuroimaging Dataset [11.173478552040441]
  Alzheimer's Disease (AD) induces both localised and widespread neural degenerative changes throughout the brain. In this work, we evaluated several unsupervised methods to train a feature extractor for downstream AD vs. CN classification.
  arXiv  Detail & Related papers  (2024-06-20T11:26:32Z)
• Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting [3.9347915104376168]
  Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error.
  arXiv  Detail & Related papers  (2024-03-21T15:52:05Z)
• 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)
• Cas-DiffCom: Cascaded diffusion model for infant longitudinal super-resolution 3D medical image completion [47.83003164569194]
  We propose a two-stage cascaded diffusion model, Cas-DiffCom, for dense and longitudinal 3D infant brain MRI completion and super-resolution. Experiment results validate that Cas-DiffCom achieves both individual consistency and high fidelity in longitudinal infant brain image completion.
  arXiv  Detail & Related papers  (2024-02-21T12:54:40Z)
• DFENet: A Novel Dimension Fusion Edge Guided Network for Brain MRI Segmentation [0.0]
  We propose a novel Dimension Fusion Edge-guided network (DFENet) that can meet both of these requirements by fusing the features of 2D and 3D CNNs. The proposed model is robust, accurate, superior to the existing methods, and can be relied upon for biomedical applications.
  arXiv  Detail & Related papers  (2021-05-17T15:43:59Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Rotation-Equivariant Deep Learning for Diffusion MRI [49.321304988619865]
  Convolutional networks are successful, but they have recently been outperformed by new neural networks that are equivariant under rotations and translations. Here we generalize them to 6D diffusion MRI data, ensuring joint equivariance under 3D roto-translations in image space and the matching 3D rotations in $q$-space. Our proposed neural networks yield better results and require fewer scans for training compared to non-rotation-equivariant deep learning.
  arXiv  Detail & Related papers  (2021-02-13T15:18:34Z)
• Interpretation of 3D CNNs for Brain MRI Data Classification [56.895060189929055]
  We extend the previous findings in gender differences from diffusion-tensor imaging on T1 brain MRI scans. We provide the voxel-wise 3D CNN interpretation comparing the results of three interpretation methods.
  arXiv  Detail & Related papers  (2020-06-20T17:56:46Z)
• Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging [7.265739747023668]
  Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. Neuro4Neuro is capable of direct extraction of WM tracts from diffusion images using convolutional neural network (CNN) This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI)
  arXiv  Detail & Related papers  (2020-05-26T16:14:31Z)

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.