Ms. Abigael Schonewille Profile

Abigael Schonewille

at Queen's Univ

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Proceedings Article | 16 March 2020 Paper

Automatic segmentation of spinal ultrasound landmarks with U-net using multiple consecutive images for input

Victoria Wu, Tamas Ungi, Kyle Sunderland, Grace Pigeau, Abigael Schonewille, Gabor Fichtinger

Proceedings Volume 11315, 1131527 (2020) https://doi.org/10.1117/12.2549584

KEYWORDS: Image segmentation, Ultrasonography, Neural networks, Convolutional neural networks, Spine, Surgery, Image processing, X-rays, Network architectures, 3D image processing

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PURPOSE: Scoliosis screening is currently only implemented in a few countries due to the lack of a safe and accurate measurement method. Spinal ultrasound is a viable alternative to X-ray, but manual annotation of images is difficult and time consuming. We propose using deep learning through a U-net neural network that takes consecutive images per individual input, as an enhancement over using single images as input for the neural network.

METHODS: Ultrasound data was collected from nine healthy volunteers. Images were manually segmented. To accommodate for consecutive input images, the ultrasound images were exported along with previous images stacked to serve as input for a modified U-net. Resulting output segmentations were evaluated based on the percentage of true negative and true positive pixel predictions.

RESULTS: After comparing the single to five-image input arrays, the three-image input had the best performance in terms of true positive value. The single input and three-input images were then further tested. The single image input neural network had a true negative rate of 99.79%, and a true positive rate of 63.56%. The three-image input neural network had a true negative rate of 99.75%, and a true positive rate of 66.64%.

CONCLUSION: The three-image input network outperformed the single input network in terms of the true positive rate by 3.08%. These findings suggest that using two additional input images consecutively preceding the original image assist the neural network in making more accurate predictions.

Proceedings Article | 16 March 2020 Presentation + Paper

Ultrasound image simulation with generative adversarial network

Grace Pigeau, Lydia Elbatarny, Victoria Wu, Abigael Schonewille, Gabor Fichtinger, Tamas Ungi

Proceedings Volume 11315, 1131508 (2020) https://doi.org/10.1117/12.2549592

KEYWORDS: Ultrasonography, Image segmentation, Kidney, Computer simulations, Fluoroscopy, Machine learning

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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 with ten simulated images not included in the first part of the survey and asked to evaluate the realism of the images. RESULTS: The average number of correctly identified images was 28 of 50 (56%). On a scale of 1-5, where 5 is indistinguishable from real US, the generated images received an average score of 3.75 for realistic anatomy and 4.0 for realistic ultrasound effects. CONCLUSIONS: We evaluated the realism of kidney ultrasound images generated using adversarial networks. Generative adversarial networks appear to be a promising method of simulating realistic ultrasound images from crosssectional anatomical label-maps.

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