{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T00:54:47Z","timestamp":1769993687060,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"U.S. Army Medical Research and Development Command"},{"name":"Science Education Programs at National Institutes of Health (NIH)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Tissue phantoms are important for medical research to reduce the use of animal or human tissue when testing or troubleshooting new devices or technology. Development of machine-learning detection tools that rely on large ultrasound imaging data sets can potentially be streamlined with high quality phantoms that closely mimic important features of biological tissue. Here, we demonstrate how an ultrasound-compliant tissue phantom comprised of multiple layers of gelatin to mimic bone, fat, and muscle tissue types can be used for machine-learning training. This tissue phantom has a heterogeneous composition to introduce tissue level complexity and subject variability in the tissue phantom. Various shrapnel types were inserted into the phantom for ultrasound imaging to supplement swine shrapnel image sets captured for applications such as deep learning algorithms. With a previously developed shrapnel detection algorithm, blind swine test image accuracy reached more than 95% accuracy when training was comprised of 75% tissue phantom images, with the rest being swine images. For comparison, a conventional MobileNetv2 deep learning model was trained with the same training image set and achieved over 90% accuracy in swine predictions. Overall, the tissue phantom demonstrated high performance for developing deep learning models for ultrasound image classification.<\/jats:p>","DOI":"10.3390\/jimaging8100270","type":"journal-article","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T23:39:31Z","timestamp":1665272371000},"page":"270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Using an Ultrasound Tissue Phantom Model for Hybrid Training of Deep Learning Models for Shrapnel Detection"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0764-519X","authenticated-orcid":false,"given":"Sofia I.","family":"Hernandez-Torres","sequence":"first","affiliation":[{"name":"U.S. Army Institute of Surgical Research, Joint Base San Antonio Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7180-2842","authenticated-orcid":false,"given":"Emily N.","family":"Boice","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, Joint Base San Antonio Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0293-4937","authenticated-orcid":false,"given":"Eric J.","family":"Snider","sequence":"additional","affiliation":[{"name":"U.S. Army Institute of Surgical Research, Joint Base San Antonio Fort Sam Houston, San Antonio, TX 78234, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"ref_1","unstructured":"American College of Emergency Physicians Council (1990). Resolution on Ultrasound. ACEP News, 9, 1\u201315."},{"key":"ref_2","unstructured":"Harper, H., and Myers, M. (2008). Military and Tactical Ultrasound. Emergency Ultrasound, ACEP."},{"key":"ref_3","unstructured":"Ma, O.J., Mateer, J.R., Reardon, R.F., and Joing, S.A. (2014). Chapter 4. Ultrasound in Prehospital and Austere Environments. Ma and Mateer\u2019s Emergency Ultrasound, The McGraw-Hill Companies."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"21","DOI":"10.7205\/MILMED-D-12-00267","article-title":"Ultrasound in the Austere Environment: A Review of the History, Indications, and Specifications","volume":"178","author":"Russell","year":"2013","journal-title":"Mil. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1016\/j.injury.2018.07.002","article-title":"Integrating Extended Focused Assessment with Sonography for Trauma (EFAST) in the Initial Assessment of Severe Trauma: Impact on the Management of 756 Patients","volume":"49","author":"Zieleskiewicz","year":"2018","journal-title":"Injury"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chakraborty, S., Murali, B., and Mitra, A.K. (2022). An Efficient Deep Learning Model to Detect COVID-19 Using Chest X-ray Images. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19042013"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"31803","DOI":"10.1007\/s11042-021-11192-5","article-title":"Deep Learning Based Detection of COVID-19 from Chest X-ray Images","volume":"80","author":"Guefrechi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9654","DOI":"10.1007\/s00330-021-08050-1","article-title":"COVID-19 Classification of X-ray Images Using Deep Neural Networks","volume":"31","author":"Keidar","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1186\/s12938-018-0587-0","article-title":"Multimodal MRI-Based Classification of Migraine: Using Deep Learning Convolutional Neural Network","volume":"17","author":"Yang","year":"2018","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40708-020-00112-2","article-title":"Application of Deep Learning in Detecting Neurological Disorders from Magnetic Resonance Images: A Survey on the Detection of Alzheimer\u2019s Disease, Parkinson\u2019s Disease and Schizophrenia","volume":"7","author":"Noor","year":"2020","journal-title":"Brain Inf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Riquelme, D., and Akhloufi, M. (2020). Deep Learning for Lung Cancer Nodules Detection and Classification in CT Scans. AI, 1.","DOI":"10.3390\/ai1010003"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"153303381988456","DOI":"10.1177\/1533033819884561","article-title":"The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods","volume":"18","author":"Li","year":"2019","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.future.2018.10.009","article-title":"Optimal Deep Learning Model for Classification of Lung Cancer on CT Images","volume":"92","author":"Lakshmanaprabu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Santosh, K.C., Dhar, M.K., Rajbhandari, R., and Neupane, A. (2020, January 28\u201330). Deep Neural Network for Foreign Object Detection in Chest X-rays. Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA.","DOI":"10.1109\/CBMS49503.2020.00107"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Deshpande, H., Harder, T., Saalbach, A., Sawarkar, A., and Buelow, T. (2020, January 3\u20137). Detection of Foreign Objects in Chest Radiographs Using Deep Learning. Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops), Iowa City, IA, USA.","DOI":"10.1109\/ISBIWorkshops50223.2020.9153350"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep Learning in Medical Ultrasound Analysis: A Review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Diaz-Escobar, J., Ord\u00f3\u00f1ez-Guill\u00e9n, N.E., Villarreal-Reyes, S., Galaviz-Mosqueda, A., Kober, V., Rivera-Rodriguez, R., and Rizk, J.E.L. (2021). Deep-Learning Based Detection of COVID-19 Using Lung Ultrasound Imagery. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0255886"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1148\/radiol.2020192224","article-title":"Preparing Medical Imaging Data for Machine Learning","volume":"295","author":"Willemink","year":"2020","journal-title":"Radiology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.ultrasmedbio.2010.02.012","article-title":"A Review of Tissue Substitutes for Ultrasound Imaging","volume":"36","author":"Culjat","year":"2010","journal-title":"Ultrasound Med. Biol."},{"key":"ref_21","first-page":"23TR01","article-title":"Tissue Mimicking Materials for Imaging and Therapy Phantoms: A Review","volume":"65","author":"McGarry","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.eml.2017.09.009","article-title":"Tissue-Mimicking Materials for Elastography Phantoms: A Review","volume":"17","author":"Cao","year":"2017","journal-title":"Extrem. Mech. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1016\/j.ultrasmedbio.2020.03.011","article-title":"Cardiac Tissue-Mimicking Ballistic Gel Phantom for Ultrasound Imaging in Clinical and Research Applications","volume":"46","author":"Alves","year":"2020","journal-title":"Ultrasound Med. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"108878","DOI":"10.1016\/j.radphyschem.2020.108878","article-title":"Composite Gelatin\/Rhizophora SPP Particleboards\/PVA for Soft Tissue Phantom Applications","volume":"173","author":"Anugrah","year":"2020","journal-title":"Radiat. Phys. Chem."},{"key":"ref_25","first-page":"044502","article-title":"Manufacturing of a Gelatin Phantom with Lymphedema for Ultrasonic Imaging Measurement","volume":"4","author":"Yoon","year":"2021","journal-title":"J. Eng. Sci. Med. Diagn. Ther."},{"key":"ref_26","first-page":"330","article-title":"Low-Cost Ultrasound and Optical Gelatin-Based Phantoms","volume":"10878","author":"Amidi","year":"2019","journal-title":"Photons Plus Ultrasound Imaging Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"09NT01","DOI":"10.1088\/1361-6560\/aabd1f","article-title":"Stable Gelatin-Based Phantom Materials with Tunable X-ray Attenuation Properties and 3D Printability for X-ray Imaging","volume":"63","author":"Dahal","year":"2018","journal-title":"Phys. Med. Biol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lhotska, L., Sukupova, L., Lackovi\u0107, I., and Ibbott, G.S. (2018, January 3\u20138). Investigating Ballistic Gelatin Based Phantom Properties for Ultrasound Training. Proceedings of the World Congress on Medical Physics and Biomedical Engineering 2018, Prague, Czech Republic.","DOI":"10.1007\/978-981-10-9038-7"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/TUFFC.2016.2594871","article-title":"Small Rodent Cardiac Phantom for Preclinical Ultrasound Imaging","volume":"64","author":"Anderson","year":"2017","journal-title":"IEEE Trans. Ultrason. Ferroelectr. Freq. Control"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1097\/AAP.0b013e31820d4207","article-title":"A Review of the Benefits and Pitfalls of Phantoms in Ultrasound-Guided Regional Anesthesia","volume":"36","author":"Hocking","year":"2011","journal-title":"Reg. Anesth. Pain. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8427","DOI":"10.1038\/s41598-022-12367-2","article-title":"An Image Classification Deep-Learning Algorithm for Shrapnel Detection from Ultrasound Images","volume":"12","author":"Snider","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1002\/jbmr.2345","article-title":"Cortical Thickness of the Femur and Long-Term Bisphosphonate Use","volume":"30","author":"Niimi","year":"2015","journal-title":"J. Bone Miner. Res."},{"key":"ref_33","unstructured":"(2021, December 17). Femur\u2014OrthopaedicsOne Review\u2014OrthopaedicsOne. Available online: https:\/\/www.orthopaedicsone.com\/display\/Review\/Femur."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1002\/mrd.22489","article-title":"The ImageJ Ecosystem: An Open Platform for Biomedical Image Analysis","volume":"82","author":"Schindelin","year":"2015","journal-title":"Mol. Reprod. Dev."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: An Open-Source Platform for Biological-Image Analysis","volume":"9","author":"Schindelin","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Boice, E.N., Hernandez-Torres, S.I., and Snider, E.J. (2022). Comparison of Ultrasound Image Classifier Deep Learning Algorithms for Shrapnel Detection. J. Imaging, 8.","DOI":"10.3390\/jimaging8050140"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Snider, E.J., Hernandez-Torres, S.I., Avital, G., and Boice, E.N. (2022). Evaluation of an Object Detection Algorithm for Shrapnel and Development of a Triage Tool to Determine Injury Severity. J. Imaging, 8.","DOI":"10.3390\/jimaging8090252"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_40","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-Cnn: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, Inc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1177\/016173468300500201","article-title":"Spectral Characterization and Attenuation Measurements in Ultrasound","volume":"5","author":"Flax","year":"1983","journal-title":"Ultrason. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1016\/j.ultrasmedbio.2020.01.031","article-title":"Experimental Measurements of Ultrasound Attenuation in Human Chest Wall and Assessment of the Mechanical Index for Lung Ultrasound","volume":"46","author":"Patterson","year":"2020","journal-title":"Ultrasound Med. Biol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ultrasmedbio.2010.10.020","article-title":"Measurement of the Ultrasound Attenuation and Dispersion in Whole Human Blood and Its Components From 0\u201370 MHz","volume":"37","author":"Treeby","year":"2011","journal-title":"Ultrasound Med. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1002\/jbmr.5650080502","article-title":"Perspectives: Ultrasound Assessment of Bone","volume":"8","author":"Kaufman","year":"1993","journal-title":"J. Bone Miner. Res."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Boice, E.N., Hernandez-Torres, S.I., Knowlton, Z.J., Berard, D., Gonzalez, J.M., and Snider, E.J. (2022). Training Ultrasound Image Classification Deep-Learning Algorithms for Pneumothorax Detection Using a Synthetic Tissue Phantom. J. Imaging, 8.","DOI":"10.3390\/jimaging8090249"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/10\/270\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:45:27Z","timestamp":1760143527000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/8\/10\/270"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":45,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["jimaging8100270"],"URL":"https:\/\/doi.org\/10.3390\/jimaging8100270","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]}}}