Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks

• URL: http://arxiv.org/abs/2501.06229v1
• Date: Wed, 08 Jan 2025 00:19:52 GMT
• Title: Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks
• Authors: Subin Erattakulangara, Karthika Kelat, Katie Burnham, Rachel Balbi, Sarah E. Gerard, David Meyer, Sajan Goud Lingala,
• Abstract summary: Manual segmentation is time intensive and susceptible to errors. This study aimed to evaluate the efficacy of deep learning algorithms for automatic vocal tract segmentation from 3D MRI.
• Score: 1.0177118388531323
• License:
• Abstract: Accurate segmentation of the vocal tract from magnetic resonance imaging (MRI) data is essential for various voice and speech applications. Manual segmentation is time intensive and susceptible to errors. This study aimed to evaluate the efficacy of deep learning algorithms for automatic vocal tract segmentation from 3D MRI.

Related papers

• Unified 3D MRI Representations via Sequence-Invariant Contrastive Learning [0.15749416770494706]
  We present a sequence-invariant self-supervised framework leveraging quantitative MRI (qMRI) Experiments on healthy brain segmentation (IXI), stroke lesion segmentation (ARC), and MRI denoising show significant gains over baseline SSL approaches. Our model also generalises effectively to unseen sites, demonstrating potential for more scalable and clinically reliable volumetric analysis.
  arXiv  Detail & Related papers  (2025-01-21T11:27:54Z)
• Multimodal Segmentation for Vocal Tract Modeling [4.95865031722089]
  Real-time magnetic resonance imaging (RT-MRI) allows measuring precise movements of internal articulators during speech. We first present a deep labeling strategy for the RT-MRI video using a vision-only segmentation approach. We then introduce a multimodal algorithm using audio to improve segmentation of vocal articulators.
  arXiv  Detail & Related papers  (2024-06-22T06:44:38Z)
• MinD-3D: Reconstruct High-quality 3D objects in Human Brain [50.534007259536715]
  Recon3DMind is an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals. We present the fMRI-Shape dataset, which includes data from 14 participants and features 360-degree videos of 3D objects. We propose MinD-3D, a novel and effective three-stage framework specifically designed to decode the brain's 3D visual information from fMRI signals.
  arXiv  Detail & Related papers  (2023-12-12T18:21:36Z)
• Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks [60.42012344842292]
  3D CNN-based models dominate the field of magnetic resonance image (MRI) analytics. In this paper, four datasets of Alzheimer's and Parkinson's disease recognition are utilized in experiments. In terms of efficiency, the video framework performs better than 3D-CNN models by 5% - 11% with 50% - 66% less trainable parameters.
  arXiv  Detail & Related papers  (2023-02-24T15:26:31Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
  Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction. These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization. We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
  arXiv  Detail & Related papers  (2022-07-18T06:01:29Z)
• CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation [3.1657395760137406]
  We propose a semi-supervised segmentation framework built upon a novel atlas-based pseudo-labeling method and a 3D deep convolutional neural network (DCNN) for 3D brain MRI segmentation. The experimental results demonstrate the superiority of the proposed framework over the baseline method both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-05-19T14:42:49Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised Anomaly Segmentation in Brain MRI [55.97060983868787]
  We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance. We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
  arXiv  Detail & Related papers  (2021-09-14T09:17:27Z)
• Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation [0.0]
  We propose an unsupervised deep learning based approach for automated segmentation. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. The proposed method has shown promising results, with a dice coefficient of 0.88 for the liver segmentation compared against manual segmentation.
  arXiv  Detail & Related papers  (2020-11-20T21:45:28Z)

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.