Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps

• URL: http://arxiv.org/abs/2104.04291v1
• Date: Fri, 9 Apr 2021 10:24:27 GMT
• Title: Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps
• Authors: Heng Fang, Xi Yang, Taichi Kin, Takeo Igarashi
• Abstract summary: Whole-brain surface extraction is an essential topic in medical imaging systems. We propose a new network architecture that incorporates knowledge of signed distance fields. We validated our newly proposed method by conducting experiments on our brain magnetic resonance imaging dataset.
• Score: 16.04543442863788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Whole-brain surface extraction is an essential topic in medical imaging systems as it provides neurosurgeons with a broader view of surgical planning and abnormality detection. To solve the problem confronted in current deep learning skull stripping methods lacking prior shape information, we propose a new network architecture that incorporates knowledge of signed distance fields and introduce an additional Laplacian loss to ensure that the prediction results retain shape information. We validated our newly proposed method by conducting experiments on our brain magnetic resonance imaging dataset (111 patients). The evaluation results demonstrate that our approach achieves comparable dice scores and also reduces the Hausdorff distance and average symmetric surface distance, thus producing more stable and smooth brain isosurfaces.

Related papers

• Enhance the Image: Super Resolution using Artificial Intelligence in MRI [10.00462384555522]
  This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI. We discuss challenges and potential future directions regarding the feasibility and reliability of deep learning-based MRI super-resolution.
  arXiv  Detail & Related papers  (2024-06-19T15:19:41Z)
• 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 Reconstruction and Parcellation of Cortical Surfaces [3.9198548406564604]
  Reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative diseases like Alzheimer's disease (AD) In this work, we propose two options, one based on a graph classification branch and another based on a novel generic 3D reconstruction loss, to augment template-deformation algorithms. We attain highly accurate parcellations with a Dice score of 90.2 (graph classification branch) and 90.4 (novel reconstruction loss) together with state-of-the-art surfaces.
  arXiv  Detail & Related papers  (2022-09-19T11:45:39Z)
• FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain [0.8376091455761259]
  A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance.
  arXiv  Detail & Related papers  (2022-08-30T16:06:07Z)
• Orientation-guided Graph Convolutional Network for Bone Surface Segmentation [51.51690515362261]
  We propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. Our approach improves over the state-of-the-art methods by 5.01% in connectivity metric.
  arXiv  Detail & Related papers  (2022-06-16T23:01:29Z)
• 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)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Context-Aware Refinement Network Incorporating Structural Connectivity Prior for Brain Midline Delineation [50.868845400939314]
  We propose a context-aware refinement network (CAR-Net) to refine and integrate the feature pyramid representation generated by the UNet. For keeping the structural connectivity of the brain midline, we introduce a novel connectivity regular loss. The proposed method requires fewer parameters and outperforms three state-of-the-art methods in terms of four evaluation metrics.
  arXiv  Detail & Related papers  (2020-07-10T14:01:20Z)
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI [47.26574993639482]
  We show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales.
  arXiv  Detail & Related papers  (2020-06-23T09:20:42Z)

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.