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In biomedical applications, this challenge is exacerbated by the dense surrounding environments with feature sizes and shapes comparable to microrobots. Herein, Motion Enhanced Multi\u2010level Tracker (MEMTrack) is introduced for detecting and tracking microrobots in dense and low\u2010contrast environments. Informed by the physics of microrobot motion, synthetic motion features for deep learning\u2010based object detection and a modified Simple Online and Real\u2010time Tracking (SORT)algorithm with interpolation are used for tracking. MEMTrack is trained and tested using bacterial micromotors in collagen (tissue phantom), achieving precision and recall of 76% and 51%, respectively. Compared to the state\u2010of\u2010the\u2010art baseline models, MEMTrack provides a minimum of 2.6\u2010fold higher precision with a reasonably high recall. MEMTrack's generalizability to unseen (aqueous) media and its versatility in tracking microrobots of different shapes, sizes, and motion characteristics are shown. Finally, it is shown that MEMTrack localizes objects with a root\u2010mean\u2010square error of less than 1.84\u2009\u03bcm and quantifies the average speed of all tested systems with no statistically significant difference from the laboriously produced manual tracking data. MEMTrack significantly advances microrobot localization and tracking in dense and low\u2010contrast settings and can impact fundamental and translational microrobotic research.<\/jats:p>","DOI":"10.1002\/aisy.202300590","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T21:54:46Z","timestamp":1708034086000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Motion Enhanced Multi\u2010Level Tracker (MEMTrack): A Deep Learning\u2010Based Approach to Microrobot Tracking in Dense and Low\u2010Contrast Environments"],"prefix":"10.1002","volume":"6","author":[{"given":"Medha","family":"Sawhney","sequence":"first","affiliation":[{"name":"Department of Computer Science Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Bhas","family":"Karmarkar","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Eric J.","family":"Leaman","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Arka","family":"Daw","sequence":"additional","affiliation":[{"name":"Department of Computer Science Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Anuj","family":"Karpatne","sequence":"additional","affiliation":[{"name":"Department of Computer Science Virginia Tech  Blacksburg VA 24061 USA"}]},{"given":"Bahareh","family":"Behkam","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering Virginia Tech  Blacksburg VA 24061 USA"},{"name":"School of Biomedical Engineering &amp; Sciences Macromolecules Innovation Institute Center for Engineered Health Center for Soft Matter and Biological Physics Virginia Tech  Blacksburg VA 24061 USA"}]}],"member":"311","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/adma.202002047"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41377-020-0323-y"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/aisy.202100279"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202002203"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.nanolett.9b02869"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3221304"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3179509"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0032969"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsnano.0c05530"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12555-019-0241-z"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/OJNANO.2020.2981824"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2089"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2016.09.016"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01507-1"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-391857-4.00009-4"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pbio.2005970"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-021-04344-9"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsb.2005.06.002"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2007.12.024"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2047"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/nmeth.2075"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12918-017-0399-z"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.20812"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bts424"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.softx.2020.100440"},{"key":"e_1_2_10_27_1","first-page":"1978","volume":"2011","author":"Rust M. 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