Grace Pigeau
Undergraduate Student
School of Computing
Queen's University
Grace is a Perk Lab alumna, curently graduate student at McGill University.
Wu, Victoria; Ungi, Tamas; Sunderland, Kyle R.; Pigeau, Grace; Schonewille, Abigael; Fichtinger, Gabor
SPIE Medical Imaging, 2020.
@conference{Wu2020a,
title = {Automatic segmentation of spinal ultrasound landmarks with U-net using multiple consecutive images for input},
author = {Victoria Wu and Tamas Ungi and Kyle R. Sunderland and Grace Pigeau and Abigael Schonewille and Gabor Fichtinger},
url = {https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/CWu2020a-manuscript.pdf},
doi = {10.1117/12.2549584},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {SPIE Medical Imaging},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Wu, Victoria; Ungi, Tamas; Sunderland, Kyle R.; Pigeau, Grace; Schonewille, Abigael; Fichtinger, Gabor
Using multiple frame U-net for automated segmentation of spinal ultrasound images Conference
18th Annual Imaging Network Ontario (ImNO) Symposium, 2020.
@conference{Wu2020b,
title = {Using multiple frame U-net for automated segmentation of spinal ultrasound images},
author = {Victoria Wu and Tamas Ungi and Kyle R. Sunderland and Grace Pigeau and Abigael Schonewille and Gabor Fichtinger},
url = {https://www.imno.ca/sites/default/files/ImNO2020Proceedings.pdf
https://labs.cs.queensu.ca/perklab/wp-content/uploads/sites/3/2024/02/Wu2020b.pdf},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {18th Annual Imaging Network Ontario (ImNO) Symposium},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Pigeau, Grace; Elbatarny, Lydia; Wu, Victoria; Schonewille, Abigael; Fichtinger, Gabor; Ungi, Tamas
Ultrasound image simulation with generative adversarial network Journal Article
In: vol. 11315, pp. 54-60, 2020.
@article{fichtinger2020g,
title = {Ultrasound image simulation with generative adversarial network},
author = {Grace Pigeau and Lydia Elbatarny and Victoria Wu and Abigael Schonewille and Gabor Fichtinger and Tamas Ungi},
url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11315/1131508/Ultrasound-image-simulation-with-generative-adversarial-network/10.1117/12.2549592.short},
year = {2020},
date = {2020-01-01},
volume = {11315},
pages = {54-60},
publisher = {SPIE},
abstract = {PURPOSE
It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to a real ultrasound image. We propose to evaluate the realism of ultrasound images simulated using a generative adversarial network.
METHODS
To achieve our goal, kidney ultrasounds were collected, and relevant anatomy was segmented to create anatomical label-maps using 3D Slicer. Adversarial networks were trained to generate ultrasound images from these labelmaps. Finally, a two-part survey of 4 participants with sonography experience was conducted to assess the realism of the generated images. The first part of the survey consisted of 50 kidney ultrasound images; half of which were real while the other half were simulated. Participants were asked to label each of the 50 ultrasound images as either real or simulated. In the second part of the survey, the participants were presented …},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
PURPOSE
It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to a real ultrasound image. We propose to evaluate the realism of ultrasound images simulated using a generative adversarial network.
METHODS
To achieve our goal, kidney ultrasounds were collected, and relevant anatomy was segmented to create anatomical label-maps using 3D Slicer. Adversarial networks were trained to generate ultrasound images from these labelmaps. Finally, a two-part survey of 4 participants with sonography experience was conducted to assess the realism of the generated images. The first part of the survey consisted of 50 kidney ultrasound images; half of which were real while the other half were simulated. Participants were asked to label each of the 50 ultrasound images as either real or simulated. In the second part of the survey, the participants were presented …
It is difficult to simulate realistic ultrasound images due to the complexity of acoustic artifacts that contribute to a real ultrasound image. We propose to evaluate the realism of ultrasound images simulated using a generative adversarial network.
METHODS
To achieve our goal, kidney ultrasounds were collected, and relevant anatomy was segmented to create anatomical label-maps using 3D Slicer. Adversarial networks were trained to generate ultrasound images from these labelmaps. Finally, a two-part survey of 4 participants with sonography experience was conducted to assess the realism of the generated images. The first part of the survey consisted of 50 kidney ultrasound images; half of which were real while the other half were simulated. Participants were asked to label each of the 50 ultrasound images as either real or simulated. In the second part of the survey, the participants were presented …