@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 {DiMaio2007b, title = {Challenges in image-guided therapy system design}, journal = {Neuroimage}, volume = {37 Suppl 1}, year = {2007}, pages = {S144{\textendash}S151}, publisher = {Brigham, Women{\textquoteright}s Hospital, 75 Francis St , Boston, MA 02115, USA simond@bwh harvard edu}, abstract = {

System development for image-guided therapy (IGT), or image-guided interventions (IGI), continues to be an area of active interest across academic, industry groups This is an emerging field that is growing rapidly: major academic institutions, medical device manufacturers have produced IGT technologies that are in routine clinical use, dozens of high-impact publications are published in well regarded journals each year, and several small companies have successfully commercialized sophisticated IGT systems In meetings between IGT investigators over the last two years, a consensus has emerged that several key areas must be addressed collaboratively by the community to reach the next level of impact, efficiency in IGT research, development to improve patient care These meetings culminated in a two-day workshop that brought together several academic, industrial leaders in the field today The goals of the workshop were to identify gaps in the engineering infrastructure available to IGT researchers, develop the role of research funding agencies, the recently established US-based National Center for Image Guided Therapy (NCIGT), and ultimately to facilitate the transfer of technology among research centers that are sponsored by the National Institutes of Health (NIH) Workshop discussions spanned many of the current challenges in the development, deployment of new IGT systems Key challenges were identified in a number of areas, including: validation standards; workflows, use-cases, and application requirements; component reusability;, device interface standards This report elaborates on these key points, proposes research challenges that are to be addressed by a joint effort between academic, industry, and NIH participants

}, keywords = {adverse effects/instrumentation/trends, Algorithms, Computational Biology, Computer Systems, Computer-Assisted, Humans, instrumentation/trends, Knowledge Bases, Models, National Institutes of Health (U S ), Neurosurgical Procedures, Organizational, Outcome Assessment (Health Care), Reproducibility of Results, Robotics, Software, Surgery, Treatment Outcome, United States}, doi = {10 1016/j neuroimage 2007 04 026}, url = {http://dx doi org/10 1016/j neuroimage 2007 04 026}, author = {Simon DiMaio and Tina Kapur and Kevin Cleary and Stephen Aylward and Peter Kazanzides and Kirby Vosburgh and Randy Ellis and James Duncan and Keyvan Farahani and Heinz Lemke and T. M. Peters and William Bill Lorensen and David Gobbi and John Haller and Laurence Larry Clarke and Stephen Pizer and Russell H. Taylor and Robert L. Galloway, Jr. and Gabor Fichtinger and Nobuhiko Hata and Kimberly Lawson and Clare M Tempany and Ron Kikinis and Ferenc Jolesz} }