Data underlying the MSc thesis: Deep learning segmentation of 3D ultrasound thyroid imaging

doi: 10.4121/265d24f7-a02f-43bd-9807-aa731dad6431.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/265d24f7-a02f-43bd-9807-aa731dad6431
Datacite citation style:
Munsterman, Roxane (2023): Data underlying the MSc thesis: Deep learning segmentation of 3D ultrasound thyroid imaging. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/265d24f7-a02f-43bd-9807-aa731dad6431.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
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Enschede
time coverage
february 2023

3D ultrasound data acquired with a matrix probe and philips system in february 2023.

Dataset_PHILIPS_save3D_and_saveMPR:

Left and right thyroid lobe scan of 57 healthy volunteers.

Data is ordered by participant numbers, containing 10 files per patient:

3 MPR files (axial, coronal and saggital orientation) per lobe (higher resolution)

1 Save3D DICOM (philips) file per lobe (more slices)

This data was not altered after collection from the US system.


Annotated 27 subjects:

Contains the first 27 subjects of the folder Dataset_PHILIPS_save3D_and_saveMPR_35GB. Philips DICOM tags were changed to regular DICOM tags and samples were rotated. Contains one annotation file per subject containing thyroid, jugular vein and cartid artery.


More information can be found in my thesis: deep learning segmentation of 3D ultrasound thyroid imaging

history
  • 2023-10-03 first online, published, posted
publisher
4TU.ResearchData
format
DICOM (philips)
organizations
University of Twente, Technical Medical (TechMed) Centre

DATA

files (3)