@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} } @conference {Baum2019a, title = {Step-wise identification of ultrasound-visible anatomical landmarks for 3D visualization of scoliotic spine}, booktitle = {SPIE Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10951}, year = {2019}, month = {03/2019}, address = {San Diego, California}, author = {Zachary M C Baum and Ben Church and Andras Lasso and Tamas Ungi and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger} } @article {Baum2018b, title = {Ultrasound-based vertebral landmark localization using deformable spine models}, year = {2018}, publisher = {Imaging Network Ontario (ImNO)}, address = {Toronto, Canada}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Christopher Schlenger and Gabor Fichtinger} } @conference {Baum2018a, title = {Visual aid for identifying vertebral landmarks in ultrasound}, booktitle = {SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling}, volume = {10576}, year = {2018}, month = {03/2018}, pages = {105760Z}, address = {Houston, Texas}, doi = {10.1117/12.2292820}, author = {Zachary M C Baum and Tamas Ungi and Andras Lasso and Ben Church and Christopher Schlenger and Gabor Fichtinger} } @article {Church2017b, title = {Scoliosis visualization using transverse process landmarks}, year = {2017}, keywords = {scoliosis, Spine, Ultrasound, visualization}, author = {Ben Church}, editor = {Andras Lasso and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger and Tamas Ungi} } @conference {Church2017, title = {Visualization of scoliotic spine using ultrasound-accessible skeletal landmarks}, booktitle = {SPIE Medical Imaging 2017}, year = {2017}, keywords = {scoliosis, Spine, Ultrasound, visualization}, author = {Ben Church and Andras Lasso and Christopher Schlenger and Daniel P. Borschneck and Parvin Mousavi and Gabor Fichtinger and Tamas Ungi} }