{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:43:30Z","timestamp":1774946610704,"version":"3.50.1"},"reference-count":13,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T00:00:00Z","timestamp":1718150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T00:00:00Z","timestamp":1718150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["01EC1906D"],"award-info":[{"award-number":["01EC1906D"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Wearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Using the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Both retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-024-03204-0","type":"journal-article","created":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T12:02:36Z","timestamp":1718193756000},"page":"1607-1614","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-484X","authenticated-orcid":false,"given":"Hannah","family":"Strohm","sequence":"first","affiliation":[]},{"given":"Sven","family":"Rothluebbers","sequence":"additional","affiliation":[]},{"given":"Luis","family":"Perotti","sequence":"additional","affiliation":[]},{"given":"Oskar","family":"Stamm","sequence":"additional","affiliation":[]},{"given":"Marc","family":"Fournelle","sequence":"additional","affiliation":[]},{"given":"Juergen","family":"Jenne","sequence":"additional","affiliation":[]},{"given":"Matthias","family":"Guenther","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,6,12]]},"reference":[{"key":"3204_CR1","doi-asserted-by":"publisher","first-page":"CD000335","DOI":"10.1002\/14651858.CD000335.pub2","volume":"3","author":"J Hayden","year":"2005","unstructured":"Hayden J, Tulder MW, Malmivaara A, Koes BW (2005) Exercise therapy for treatment of non-specific low back pain. Cochrane Database Syst Rev 3:CD000335. https:\/\/doi.org\/10.1002\/14651858.CD000335.pub2","journal-title":"Cochrane Database Syst Rev"},{"issue":"4","key":"3204_CR2","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.clinbiomech.2004.01.007","volume":"19","author":"JM McMeeken","year":"2004","unstructured":"McMeeken JM, Beith ID, Newham DJ, Milligan P, Critchley DJ (2004) The relationship between EMG and change in thickness of transversus abdominis. Clin Biomech 19(4):337\u2013342. https:\/\/doi.org\/10.1016\/j.clinbiomech.2004.01.007","journal-title":"Clin Biomech"},{"key":"3204_CR3","doi-asserted-by":"publisher","first-page":"346","DOI":"10.2519\/jospt.2005.35.6.346","volume":"35","author":"DS Teyhen","year":"2005","unstructured":"Teyhen DS, Miltenberger CE, Deiters HM, Del Toro YM, Pulliam JN, Childs JD, Boyles RE, Flynn TW (2005) The use of ultrasound imaging of the abdominal drawing-in maneuver in subjects with low back pain. J Orthop Sports Phys Ther 35:346\u201355. https:\/\/doi.org\/10.2519\/jospt.2005.35.6.346","journal-title":"J Orthop Sports Phys Ther"},{"key":"3204_CR4","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1186\/s12891-015-0674-3","volume":"16","author":"J L\u00fckens","year":"2015","unstructured":"L\u00fckens J, Bostr\u00f6m KJ, Puta C, Schulte TL, Wagner H (2015) Using ultrasound to assess the thickness of the transversus abdominis in a sling exercise. BMC Musculoskelet Disord 16:203. https:\/\/doi.org\/10.1186\/s12891-015-0674-3","journal-title":"BMC Musculoskelet Disord"},{"key":"3204_CR5","doi-asserted-by":"publisher","first-page":"338","DOI":"10.2519\/jospt.2005.35.6.338","volume":"35","author":"SM Henry","year":"2005","unstructured":"Henry SM, Westervelt KC (2005) The use of real-time ultrasound feedback in teaching abdominal hollowing exercises to healthy subjects. J Orthop Sports Phys Ther 35:338\u2013345. https:\/\/doi.org\/10.2519\/jospt.2005.35.6.338","journal-title":"J Orthop Sports Phys Ther"},{"key":"3204_CR6","doi-asserted-by":"publisher","unstructured":"Ling S, Zhou Y, Chen Y, Zhao Y-Q, Wang L, Zheng Y-P (2013) Automatic tracking of aponeuroses and estimation of muscle thickness in ultrasonography: a feasibility study. IEEE J Biomed Health Inform 17(6):1031\u20131038. https:\/\/doi.org\/10.1109\/JBHI.2013.2253787","DOI":"10.1109\/JBHI.2013.2253787"},{"key":"3204_CR7","doi-asserted-by":"publisher","unstructured":"Saleh A, Laradji IH, Lammie C, Vazquez D, Flavell CA, Azghadi MR (2021) A deep learning localization method for measuring abdominal muscle dimensions in ultrasound images. IEEE J Biomed Health Inform 25(10):3865\u20133873. https:\/\/doi.org\/10.1109\/JBHI.2021.3085019","DOI":"10.1109\/JBHI.2021.3085019"},{"key":"3204_CR8","doi-asserted-by":"publisher","unstructured":"Katakis S, Barotsis N, Kakotaritis A, Tsiganos P, Economou G, Panagiotopoulos EC, Panayiotakis GS (2023) Muscle cross-sectional area segmentation in transverse ultrasound images using vision transformers. Diagnostics 13:217. https:\/\/doi.org\/10.3390\/diagnostics13020217","DOI":"10.3390\/diagnostics13020217"},{"key":"3204_CR9","doi-asserted-by":"publisher","unstructured":"Howard A, Sandler M, Chen B, Wang W, Chen L-C, Tan M, Chu G, Vasudevan V, Zhu Y, Pang R, Adam H, Le Q (2019) Searching for MobileNetV3. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 1314\u20131324. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"2","key":"3204_CR10","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/info11020125","volume":"11","author":"A Buslaev","year":"2020","unstructured":"Buslaev A, Iglovikov VI, Khvedchenya E, Parinov A, Druzhinin M, Kalinin AA (2020) Albumentations: Fast and flexible image augmentations. Information 11(2):125. https:\/\/doi.org\/10.3390\/info11020125","journal-title":"Information"},{"key":"3204_CR11","unstructured":"Bouguet J-Y (1999) Pyramidal implementation of the Lucas Kanade feature tracker"},{"key":"3204_CR12","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention (MICCAI). LNCS, vol 9351, pp 234\u2013241 https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3204_CR13","doi-asserted-by":"publisher","DOI":"10.5555\/2969239.2969329","volume-title":"Advances in neural information processing systems","author":"X Shi","year":"2015","unstructured":"Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-k, Woo W-c (2015) Convolutional lstm network: a machine learning approach for precipitation nowcasting. In: Cortes C, Lawrence N, Lee D, Sugiyama M, Garnett R (eds) Advances in neural information processing systems. Curran Associates, New York. https:\/\/doi.org\/10.5555\/2969239.2969329"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03204-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03204-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03204-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T10:06:29Z","timestamp":1732529189000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03204-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,12]]},"references-count":13,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["3204"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03204-0","relation":{},"ISSN":["1861-6429"],"issn-type":[{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,12]]},"assertion":[{"value":"12 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}