mlsanityData to 7 Oct 2026

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

arXiv 2501.06229 ↗First posted 8 Jan 2025cs.CV cs.SD eess.AScited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 52 references that are in the map. First posted on arXiv 8 Jan 2025.

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References · 7

  1. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  2. 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
  3. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  4. 2022MONAI: An open-source framework for deep learning in healthcarecited by 438
  5. 2019Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practicecited by 71
  6. 2021A multispeaker dataset of raw and reconstructed speech production real-time MRI video and 3D volumetric imagescited by 21
  7. 20233D TransUNet: Advancing Medical Image Segmentation through Vision Transformerscited by 20

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