Automatic spine ultrasound segmentation for scoliosis visualization and measurement

TitleAutomatic spine ultrasound segmentation for scoliosis visualization and measurement
Publication TypeJournal Article
Year of PublicationIn Press
AuthorsUngi, T., Greer H., Sunderland K. R., Wu V., Baum Z. M. C., Schlenger C., Oetgen M., Cleary K., Aylward S., & Fichtinger G.
JournalIEEE Transactions on Biomedical Engineering
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° 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.

URLhttps://ieeexplore.ieee.org/document/9034149
DOI10.1109/TBME.2020.2980540
PerkWeb Citation KeyUngi2020