DOI: 10.6084/m9.figshare.22191217.v2
title: 3D Upper airway training dataset

description: This dataset is a derivative of the original French speaker database, designed for training and testing deep learning models for upper airway segmentation. It includes upper airway MRI scans and their corresponding expert-crafted annotations, specifically created for the upper airway segmentation project. The dataset also features synthetic sentences to ensure adequate coverage of the French phonetic context. Each 3D scan, acquired using a Siemens Prisma 3T scanner with a VIBE sequence (TR = 3.8 ms, TE = 1.55 ms, FOV = 22 × 22 cm², slice thickness = 1.2 mm, and image size = 320 × 290 × 36 slices), has a duration of 7 seconds.The segmentations were manually created in a two-stage process by an annotator team with expertise in voice-science. First, the volumes were distributed to two senior graduate students in voice science who performed the segmentations. Next, the segmentations were further refined by an expert vocologist with more than 20 years experience in voice pedagogy. All processing was done in the Slicer environment. Subsequently, the segmentations and the original MRI volumes underwent conversion into NRRD format. For training, we have used a subset of 45 volumes across 7 subjects from these 53 volumes.For the test set, assessing the performance of an algorithm using annotated label maps from a single human expert as a reference poses challenges in evaluation due to potential bias introduced by the human. To mitigate this challenge, we employed the STAPLE algorithm. This algorithm considers a collection of segmentations from multiple human annotators, and computes a probabilistic estimate of the true segmentation. In creating the test set, we chose 8 volumes from three subjects (non-overlapping with the training set), and collected manual segmentations from three different annotators. The first two annotators were senior graduate students with expertise in image processing and biomedical engineering, and segmentations from a third annotator were those provided by the voice-science team as described above. These segmentations were then input into the STAPLE algorithm to generate an optimal segmentation. The STAPLE outputs are provided in the test set.Dataset Structure<b>Training:</b><b>RTrainVolumes:</b> 3D upper airway MRI volumes.<b>RtrainLabels:</b> Corresponding segmentation annotations.<b>Testing:</b><b>RVolumes:</b> 3D upper airway MRI volumes.<b>RLabels:</b> Corresponding segmentation annotations.CitationIf you use this dataset, please cite the following papers:Subin Erattakulangara, Karthika Kelat, Katie Burnham, Rachel Balbi, Sarah E. Gerard, David Meyer, Sajan Goud Lingala,Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks, Journal of Voice, 2025, ISSN 0892-1997, https://doi.org/10.1016/j.jvoice.2025.02.026.Isaieva, Karyna, et al. "Multimodal dataset of real-time 2D and static 3D MRI of healthy French speakers." <i>Scientific Data</i>, 8.1 (2021): 258.Erattakulangara, Subin, et al. "Automatic multiple articulator segmentation in dynamic speech MRI using a protocol adaptive stacked transfer learning u-net model." <i>Bioengineering</i> 10.5 (2023): 623.<br>