Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset

Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset

Citation Author(s):
Ryan
Cunningham
Manchester Metropolitan University
María B.
Sánchez
Manchester Metropolitan University
Ian D.
Loram
Manchester Metropolitan University
Submitted by:
Ryan Cunningham
Last updated:
Fri, 03/01/2019 - 17:49
DOI:
10.21227/bwm7-3x07
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Abstract: 

Segmentation of ultrasound (US) images of cervical muscles is required for visualisation, analysis of deep, muscle structure and function, and monitoring of patient specific treatment protocols. The lack of publicly available labelled data, benchmark methods and results, hinders development of this domain. Here, we contribute a labelled dataset of 1,100 US images of the human posterior cervical muscles during head motion, from 28 healthy adults (19 male, mean 31.4, s.d. 8.9), and a labelled static MRI/ultrasound dataset of human posterior cervical muscles, from 25 healthy adults (age: 29.0 ± 6.6, male: 12).

Instructions: 

There are two folders in the zip file:

 

data_head_motion folder:

Contains a set of ultrasound neck muscle images (uchar format), and a set of correspnding semantic segmentation label images (uchar format). These images were acquired from 28 healthy participants during a head motion tracking task [1] - there are approximately 40 images per participant spanning their range of motion, fully labelled by expert operators.

 

data_static folder:

Contains a set of MRI and matched ultrasound posterior neck muscle images (uchar format), and a set of correspnding semantic segmentation label images (uchar format) for both MRI and ultrasound images. These images were acquired from 25 healthy participants during a static standing (ultrasound), and static lying (MRI) [2] - there is one MRI and one ultrasound image per participant, and two sets of labels representing annotation from two different expert operators. The acquisition plane of the ultrasound was marked by cod liver oil capsules, and the MRI was acquired in the same plane. Both images are in the axial plane at around C3-5 in the spine (see [2] for details).

 

See read_me.txt filed within the dataset folders for a description of the filename convention.

 

[1] R. Cunningham, M. B. Sánchezand I. D. Loram, “Ultrasound Segmentation of Cervical Muscle during head motion: a Dataset and a Benchmark using Deconvolutional Neural Networks”, 26-Feb-2019. [Online]. Available: engrxiv.org/fsa3c.

[2] Cunningham, R.J., Harding, P.J. and Loram, I.D., 2017. Real-time ultrasound segmentation, analysis and visualisation of deep cervical muscle structure. IEEE transactions on medical imaging36(2), pp.653-665.

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[1] Ryan Cunningham, María B. Sánchez, Ian D. Loram, "Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset", IEEE Dataport, 2019. [Online]. Available: http://dx.doi.org/10.21227/bwm7-3x07. Accessed: May. 22, 2019.
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doi = {10.21227/bwm7-3x07},
url = {http://dx.doi.org/10.21227/bwm7-3x07},
author = {Ryan Cunningham; María B. Sánchez; Ian D. Loram },
publisher = {IEEE Dataport},
title = {Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset},
year = {2019} }
TY - DATA
T1 - Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset
AU - Ryan Cunningham; María B. Sánchez; Ian D. Loram
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PB - IEEE Dataport
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Ryan Cunningham, María B. Sánchez, Ian D. Loram. (2019). Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset. IEEE Dataport. http://dx.doi.org/10.21227/bwm7-3x07
Ryan Cunningham, María B. Sánchez, Ian D. Loram, 2019. Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset. Available at: http://dx.doi.org/10.21227/bwm7-3x07.
Ryan Cunningham, María B. Sánchez, Ian D. Loram. (2019). "Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset." Web.
1. Ryan Cunningham, María B. Sánchez, Ian D. Loram. Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset [Internet]. IEEE Dataport; 2019. Available from : http://dx.doi.org/10.21227/bwm7-3x07
Ryan Cunningham, María B. Sánchez, Ian D. Loram. "Ultrasound/MRI Neck Muscle Image Semantic Segmentation Dataset." doi: 10.21227/bwm7-3x07