{"id":2474,"date":"2024-05-03T20:21:12","date_gmt":"2024-05-03T20:21:12","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/victoria-wu\/"},"modified":"2024-05-03T20:21:12","modified_gmt":"2024-05-03T20:21:12","slug":"victoria-wu","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/victoria-wu\/","title":{"rendered":"Victoria Wu"},"content":{"rendered":"
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Victoria Wu<\/h1>\n<\/div>\n
Undergraduate Student<\/div>\n
School of Computing<\/div>\n
Queen’s University<\/div>\n
Member from 2018<\/em> to present<\/em><\/div>\n
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Victoria is a Perk Lab alumna, she was an undegraduate student in the Cognitive Science (COGS) Honor’s program at the Queen’s School of Computing. She curently works at Microsoft.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

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Ungi, Tamas; Greer, Hastings; Sunderland, Kyle R.; Wu, Victoria; Baum, Zachary M C; Schlenger, Christopher; Oetgen, Matthew; Cleary, Kevin; Aylward, Stephen; Fichtinger, Gabor<\/p>

Automatic spine ultrasound segmentation for scoliosis visualization and measurement<\/a> Journal Article<\/span> <\/p>

In: <\/span>IEEE Transactions on Biomedical Engineering, <\/span>vol. 67, <\/span>no. 11, <\/span>pp. 3234 - 3241, <\/span>2020<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{Ungi2020,
\r\ntitle = {Automatic spine ultrasound segmentation for scoliosis visualization and measurement},
\r\nauthor = {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},
\r\nurl = {https:\/\/ieeexplore.ieee.org\/document\/9034149
\r\nhttps:\/\/labs.cs.queensu.ca\/perklab\/wp-content\/uploads\/sites\/3\/2024\/02\/Ungi2020.pdf},
\r\ndoi = {10.1109\/TBME.2020.2980540},
\r\nyear  = {2020},
\r\ndate = {2020-03-01},
\r\nurldate = {2020-03-01},
\r\njournal = {IEEE Transactions on Biomedical Engineering},
\r\nvolume = {67},
\r\nnumber = {11},
\r\npages = {3234 - 3241},
\r\nabstract = {<p>\\emph{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. \\emph{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. \\emph{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\u00b0 compared to X-ray. \\emph{Conclusion:} automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. \\emph{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.<\/p>},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
\r\n<\/pre><\/div>
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<p>Objective:<\/em> 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:<\/em> 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:<\/em> 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\u00b0 compared to X-ray. Conclusion:<\/em> automatic spine segmentation makes scoliosis measurement both efficient and accurate in tracked ultrasound scans. Significance:<\/em> Automatic segmentation may overcome the limitations of tracked ultrasound that so far prevented its use as an alternative of X-ray in scoliosis measurement.<\/p><\/div>
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