@conference {Wu2020a, title = {Automatic segmentation of spinal ultrasound landmarks with U-net using multiple consecutive images for input}, booktitle = {SPIE Medical Imaging}, year = {2020}, doi = {10.1117/12.2549584}, author = {Victoria Wu and Tamas Ungi and Kyle R. Sunderland and Grace Pigeau and Abigael Schonewille and Gabor Fichtinger} } @article {Ungi2020, title = {Automatic spine ultrasound segmentation for scoliosis visualization and measurement}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {67}, number = {11}, year = {2020}, month = {03/2020}, pages = {3234 - 3241}, abstract = {

Objective: Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging. Methods: We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers. We tested the trained network on eight pediatric patients. We evaluated image segmentation and 3-dimensional volume reconstruction for scoliosis measurement. Results: As expected, fuzzy segmentation metrics reduced when trained networks were translated from healthy volunteers to patients. Recall decreased from 0.72 to 0.64 (8.2\% decrease), and precision from 0.31 to 0.27 (3.7\% decrease). However, after finding optimal thresholds for prediction maps, binary segmentation metrics performed better on patient data. Recall decreased from 0.98 to 0.97 (1.6\% decrease), and precision from 0.10 to 0.06 (4.5\% decrease). Segmentation prediction maps were reconstructed to 3-dimensional volumes and scoliosis was measured in all patients. Measurement in these reconstructions took less than 1 minute and had a maximum error of 2.2{\textdegree} compared to X-ray. Conclusion: automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. Significance: Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.

}, doi = {10.1109/TBME.2020.2980540}, url = {https://ieeexplore.ieee.org/document/9034149}, author = {Tamas Ungi and Hastings Greer and Kyle R. Sunderland and Victoria Wu and Zachary M C Baum and Christopher Schlenger and Matthew Oetgen and Kevin Cleary and Stephen Aylward and Gabor Fichtinger} } @article {Wu2020b, title = {Using multiple frame U-net for automated segmentation of spinal ultrasound images}, year = {2020}, url = {https://www.imno.ca/sites/default/files/ImNO2020Proceedings.pdf}, author = {Victoria Wu and Tamas Ungi and Kyle R. Sunderland and Grace Pigeau and Abigael Schonewille and Gabor Fichtinger} } @article {Wu2019b, title = {Detection of Spinal Ultrasound Landmarks Using Convolutional Neural Networks}, volume = {14}, year = {2019}, publisher = {Int J CARS}, address = {Rennes, France}, doi = {https://doi.org/10.1007/s11548-019-01969-3}, author = {Victoria Wu and Mark Asselin and Tamas Ungi and Gabor Fichtinger} } @article {Wu2019a, title = {Using Deep Learning for Transverse Process Detection in Spinal Ultrasounds}, year = {2019}, publisher = {Imaging Network Ontario (ImNO)}, address = {London, Ontario}, author = {Victoria Wu and Tamas Ungi and Gabor Fichtinger} }