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examples/2023botrosRAL.bib

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+
+@STRING{Science_Robotics      = "Sci. Robot."}
+@STRING{AIS                   = "Adv. Int.Syst"}
+@STRING{AMT                   = "Adv. Mater. Technol."}
+@STRING{IJO                   = "Int. J. Optomechatron."}
+@STRING{ICRA                  = "Proc. IEEE Int. Conf. Robot. Automat."}
+
+@STRING{IEEE_J_ITS        = "{IEEE} Trans. Intell. Transp. Syst."}
+@STRING{IEEE_J_BME        = "{IEEE} Trans. Biomed. Eng."}
+@STRING{IEEE_J_RAL        = "{IEEE} Robot. Autom. Lett."}
+@STRING{IEEE_J_RO         = "{IEEE} Trans. Robot."}
+
+@article{li2017micro,
+  title = {Micro/Nanorobots for Biomedicine: {{Delivery}}, Surgery, Sensing, and Detoxification},
+  author = {Li, Jinxing et al.},
+  date = {2017},
+  journaltitle = Science_Robotics,
+  volume = {2},
+  number = {4},
+  keywords = {cancer},
+}
+@article{chung2021magnetically,
+  title = {Magnetically {{Controlled Soft Robotics Utilizing Elastomers}} and {{Gels}} in {{Actuation}}: {{A Review}}},
+  shorttitle = {Magnetically {{Controlled Soft Robotics Utilizing Elastomers}} and {{Gels}} in {{Actuation}}},
+  author = {Chung, Hyun-Joong and Parsons, Andrew M. and Zheng, Lelin},
+  date = {2021},
+  journaltitle = AIS,
+  volume = {3},
+  number = {3},
+  pages = {2000186},
+  issn = {2640-4567},
+  doi = {10.1002/aisy.202000186},
+}
+@article{pane2019imaging,
+  title = {Imaging {{Technologies}} for {{Biomedical Micro-}} and {{Nanoswimmers}}},
+  author = {Pané, Salvador and Puigmartí-Luis, Josep and Bergeles, Christos and Chen, Xiang-Zhong and Pellicer, Eva and Sort, Jordi and Počepcová, Vanda and Ferreira, Antoine and Nelson, Bradley J.},
+  date = {2019},
+  journaltitle = AMT,
+  volume = {4},
+  number = {4},
+  pages = {1800575},
+  issn = {2365-709X},
+  doi = {10.1002/admt.201800575},
+}
+@inproceedings{scheggi2017magnetic,
+  title = {Magnetic Motion Control and Planning of Untethered Soft Grippers Using Ultrasound Image Feedback},
+  booktitle = ICRA,
+  author = {Scheggi, Stefano and Chandrasekar, Krishna Kumar T. and Yoon, ChangKyu and Sawaryn, Ben and family=Steeg, given=Gert, prefix=van de, useprefix=true and Gracias, David H. and Misra, Sarthak},
+  date = {2017-05},
+  pages = {6156--6161},
+  doi = {10.1109/ICRA.2017.7989730},
+}
+@article{khalil2018mechanical,
+  title = {Mechanical {{Rubbing}} of {{Blood Clots Using Helical Robots Under Ultrasound Guidance}}},
+  author = {Khalil, Islam S. M. and Mahdy, Dalia and Sharkawy, Ahmed El and Moustafa, Ramez R. and Tabak, Ahmet Fatih and Mitwally, Mohamed E. and Hesham, Sarah and Hamdi, Nabila and Klingner, Anke and Mohamed, Abdelrahman and Sitti, Metin},
+  date = {2018-04},
+  journaltitle = IEEE_J_RAL,
+  volume = {3},
+  number = {2},
+  pages = {1112--1119},
+  issn = {2377-3766},
+  doi = {10.1109/LRA.2018.2792156},
+  eventtitle = {{{IEEE Robotics}} and {{Automation Letters}}},
+}
+@article{hu2018smallscale,
+  title = {Small-Scale Soft-Bodied Robot with Multimodal Locomotion},
+  author = {Hu, Wenqi and Lum, Guo Zhan and Mastrangeli, Massimo and Sitti, Metin},
+  date = {2018-02},
+  journaltitle = {Nature},
+  volume = {554},
+  number = {7690},
+  pages = {81--85},
+  publisher = {{Nature Publishing Group}},
+  issn = {1476-4687},
+  doi = {10.1038/nature25443},
+}
+@article{ackermann2016detection,
+  title = {Detection and {{Tracking}} of {{Multiple Microbubbles}} in {{Ultrasound B-Mode Images}}},
+  author = {Ackermann, Dimitri and Schmitz, Georg},
+  date = {2016-01},
+  journaltitle = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},
+  volume = {63},
+  number = {1},
+  pages = {72--82},
+  issn = {1525-8955},
+  doi = {10.1109/TUFFC.2015.2500266},
+}
+@article{zhang2021kalman,
+  title = {A {{Kalman Filter-Based Kernelized Correlation Filter Algorithm}} for {{Pose Measurement}} of a {{Micro-Robot}}},
+  author = {Zhang, Heng and Zhan, Hongwu and Zhang, Libin and Xu, Fang and Ding, Xinbin},
+  date = {2021-07},
+  journaltitle = {Micromachines},
+  volume = {12},
+  number = {7},
+  pages = {774},
+  publisher = {{Multidisciplinary Digital Publishing Institute}},
+  issn = {2072-666X},
+  doi = {10.3390/mi12070774},
+  issue = {7},
+}
+@article{tiryaki2022deep,
+  title = {Deep {{Learning-based 3D Magnetic Microrobot Tracking}} Using {{2D MR Images}}},
+  author = {Tiryaki, Mehmet Efe and Demir, Sinan Ozgun and Sitti, Metin},
+  date = {2022-07},
+  journaltitle = IEEE_J_RAL,
+  volume = {7},
+  number = {3},
+  pages = {6982--6989},
+  issn = {2377-3766},
+  doi = {10.1109/LRA.2022.3179509},
+}
+@inproceedings{khalil2014magneticbase,
+  ids = {c11},
+  title = {Magnetic-Based Closed-Loop Control of Paramagnetic Microparticles Using Ultrasound Feedback},
+  booktitle = ICRA,
+  author = {Khalil, Islam S. M. and Ferreira, Pedro and Eleutério, Ricardo and family=Korte, given=Chris L., prefix=de, useprefix=true and Misra, Sarthak},
+  date = {2014},
+  pages = {3807--3812},
+  issn = {1050-4729},
+  doi = {10.1109/ICRA.2014.6907411},
+}
+@article{chen2019ultrasound,
+  title = {Ultrasound Tracking of the Acoustically Actuated Microswimmer},
+  author = {Chen, Qiyang and Liu, Fang-Wei and Xiao, Zunding and Sharma, Nitin and Cho, Sung Kwon and Kim, Kang},
+  date = {2019},
+  journaltitle = IEEE_J_BME,
+  volume = {66},
+  number = {11},
+  pages = {3231--3237},
+  doi = {10.1109/TBME.2019.2902523},
+}
+@article{sanchez2014magnetic,
+  title = {Magnetic Control of Self-Propelled Microjets under Ultrasound Image Guidance},
+  author = {Sánchez, Alonso and Magdanz, Veronika and Schmidt, Oliver G. and Misra, Sarthak},
+  date = {2014},
+  journaltitle = {IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics},
+  pages = {169--174},
+  issn = {2155-1774},
+  doi = {10.1109/BIOROB.2014.6913771},
+}
+@article{magdanz2020ironsperm,
+  title = {{{IRONSperm}}: {{Sperm-templated}} Soft Magnetic Microrobots},
+  shorttitle = {{{IRONSperm}}},
+  author = {Magdanz, Veronika and Khalil, Islam S. M. and Simmchen, Juliane and Furtado, Guilherme P. and Mohanty, Sumit and Gebauer, Johannes and Xu, Haifeng and Klingner, Anke and Aziz, Azaam and Medina-Sánchez, Mariana and Schmidt, Oliver G. and Misra, Sarthak},
+  date = {2020},
+  journaltitle = {Science Advances},
+  volume = {6},
+  number = {28},
+  publisher = {{American Association for the Advancement of Science}},
+  doi = {10.1126/sciadv.aba5855},
+}
+@article{pane2021realtime,
+  title = {Real-Time Imaging and Tracking of Microrobots in Tissues Using Ultrasound Phase Analysis},
+  author = {Pane, S. and Iacovacci, V. and Sinibaldi, E. and Menciassi, A.},
+  date = {2021-01-06},
+  journaltitle = {Applied Physics Letters},
+  shortjournal = {Applied Physics Letters},
+  volume = {118},
+  number = {1},
+  pages = {014102},
+  issn = {0003-6951},
+  doi = {10.1063/5.0032969},
+}
+
+@article{wang2020realtime,
+  title = {Real-{{Time Magnetic Navigation}} of a {{Rotating Colloidal Microswarm Under Ultrasound Guidance}}},
+  author = {Wang, Qianqian and Yang, Lidong and Yu, Jiangfan and Chiu, Philip Wai Yan and Zheng, Yong-Ping and Zhang, Li},
+  date = {2020-12},
+  journaltitle = IEEE_J_BME,
+  volume = {67},
+  number = {12},
+  pages = {3403--3412},
+  issn = {1558-2531},
+  doi = {10.1109/TBME.2020.2987045},
+}
+@article{wang2021ultrasound,
+  ids = {c17},
+  title = {Ultrasound {{Doppler-guided}} Real-Time Navigation of a Magnetic Microswarm for Active Endovascular Delivery},
+  author = {Wang, Qianqian and Chan, Kai Fung and Schweizer, Kathrin and Du, Xingzhou and Jin, Dongdong and Yu, Simon Chun Ho and Nelson, Bradley J. and Zhang, Li},
+  date = {2021-02-26},
+  journaltitle = {Science Advances},
+  volume = {7},
+  number = {9},
+  pages = {eabe5914},
+  publisher = {{American Association for the Advancement of Science}},
+  doi = {10.1126/sciadv.abe5914},
+}
+
+@article{liu2022capsule,
+  title = {Capsule Robot Pose and Mechanism State Detection in Ultrasound Using Attention-Based Hierarchical Deep Learning},
+  author = {Liu, Xiaoyun and Esser, Daniel and Wagstaff, Brandon and Zavodni, Anna and Matsuura, Naomi and Kelly, Jonathan and Diller, Eric},
+  date = {2022-12-07},
+  journaltitle = {Scientific Reports},
+  shortjournal = {Sci Rep},
+  volume = {12},
+  number = {1},
+  pages = {21130},
+  publisher = {{Nature Publishing Group}},
+  issn = {2045-2322},
+  doi = {10.1038/s41598-022-25572-w},
+  issue = {1},
+}
+@inproceedings{botross2022fully,
+  title = {Fully {{Automatic}} and {{Real-Time Microrobot Detection}} and {{Tracking}} Based on {{Ultrasound Imaging}} Using {{Deep Learning}}},
+  booktitle = ICRA,
+  author = {Botross, Karim and Alkhatib, Mohammad and Folio, David and FERREIRA, Antoine},
+  date = {2022-07-12},
+  publisher = {{IEEE}},
+  location = {{Philadelphia, US}},
+  doi = {10.1109/ICRA46639.2022.9812114},
+}
+@article{kummer2010octomag,
+  title = {Octomag: {{An}} Electromagnetic System for 5-{{DOF}} Wireless Micromanipulation},
+  author = {Kummer, Michael Philipp and Abbott, Jake J. and Kratochvil, Bradley E. and Borer, Ruedi and Sengul, Ali and Nelson, Bradley J.},
+  date = {2010},
+  journaltitle = IEEE_J_RO,
+  volume = {26},
+  number = {6},
+  pages = {1006--1017},
+  issn = {15523098},
+  doi = {10.1109/TRO.2010.2073030},
+}
+@article{mathieu2010steeringa,
+  title = {Steering of Aggregating Magnetic Microparticles Using Propulsion Gradients Coils in an {{MRI Scanner}}},
+  author = {Mathieu, Jean-Baptiste and Martel, Sylvain},
+  date = {2010},
+  journaltitle = {Magnetic Resonance in Medicine},
+  volume = {63},
+  number = {5},
+  pages = {1336--1345},
+  issn = {1522-2594},
+  doi = {10.1002/mrm.22279},
+}
+@article{ghanbari2014electromagnetic,
+  title = {Electromagnetic {{Steering}} of a {{Magnetic Cylindrical Microrobot Using Optical Feedback Closed-Loop Control}}},
+  author = {Ghanbari, Ali and Chang, Pyung H. and Nelson, Bradley J. and Choi, Hongsoo},
+  date = {2014-04-03},
+  journaltitle = IJO,
+  volume = {8},
+  number = {2},
+  pages = {129--145},
+  publisher = {{Taylor \& Francis}},
+  issn = {1559-9612},
+  doi = {10.1080/15599612.2014.901454},
+}
+@article{yang20203d,
+  title = {3-{{D Visual Servoing}} of {{Magnetic Miniature Swimmers Using Parallel Mobile Coils}}},
+  author = {Yang, Zhengxin and Yang, Lidong and Zhang, Li},
+  date = {2020-11},
+  journaltitle = {IEEE Trans. Med. Robot. Bionics},
+  volume = {2},
+  number = {4},
+  pages = {608--618},
+  issn = {2576-3202},
+  doi = {10.1109/TMRB.2020.3033020}
+}
+@article{hu2018smallscale,
+  title = {Small-Scale Soft-Bodied Robot with Multimodal Locomotion},
+  author = {Hu, Wenqi and Lum, Guo Zhan and Mastrangeli, Massimo and Sitti, Metin},
+  date = {2018-02},
+  journaltitle = {Nature},
+  volume = {554},
+  number = {7690},
+  pages = {81--85},
+  publisher = {{Nature Publishing Group}},
+  issn = {1476-4687},
+  doi = {10.1038/nature25443},
+  issue = {7690},
+}
+@article{bi2018design,
+  title = {Design of {{Microscale Magnetic Tumbling Robots}} for {{Locomotion}} in {{Multiple Environments}} and {{Complex Terrains}}},
+  author = {Bi, Chenghao and Guix, Maria and Johnson, Benjamin V. and Jing, Wuming and Cappelleri, David J.},
+  date = {2018-02},
+  journaltitle = {Micromachines},
+  volume = {9},
+  number = {2},
+  pages = {68},
+  publisher = {{Multidisciplinary Digital Publishing Institute}},
+  issn = {2072-666X},
+  doi = {10.3390/mi9020068},
+  issue = {2},
+}
+
+@inproceedings{youakim2015paramagnetic,
+  title = {Paramagnetic Microparticles Sliding on a Surface: {{Characterization}} and Closed-Loop Motion Control},
+  shorttitle = {Paramagnetic Microparticles Sliding on a Surface},
+  booktitle = ICRA,
+  author = {Youakim, Kareem and Ehab, Mohamed and Hatem, Omar and Misra, Sarthak and Khalil, Islam S. M.},
+  date = {2015-05},
+  pages = {4068--4073},
+  issn = {1050-4729},
+  doi = {10.1109/ICRA.2015.7139768},
+}
+@article{li2020propulsiona,
+  title = {Propulsion {{Mechanism}} of {{Flexible Microbead Swimmers}} in the {{Low Reynolds Number Regime}}},
+  author = {Li, Yan-Hom and Chen, Shao-Chun},
+  date = {2020-12},
+  journaltitle = {Micromachines},
+  volume = {11},
+  number = {12},
+  pages = {1107},
+  publisher = {{Multidisciplinary Digital Publishing Institute}},
+  issn = {2072-666X},
+  doi = {10.3390/mi11121107},
+  issue = {12},}
+}
+@article{srivastava2021comparative,
+  title = {Comparative Analysis of Deep Learning Image Detection Algorithms},
+  author = {Srivastava, Shrey and Divekar, Amit Vishvas and Anilkumar, Chandu and Naik, Ishika and Kulkarni, Ved and Pattabiraman, V.},
+  date = {2021-05-10},
+  journaltitle = {Journal of Big Data},
+  volume = {8},
+  number = {1},
+  pages = {66},
+  issn = {2196-1115},
+  doi = {10.1186/s40537-021-00434-w},
+}
+@article{marvasti-zadeh2022deep,
+  title = {Deep {{Learning}} for {{Visual Tracking}}: {{A Comprehensive Survey}}},
+  shorttitle = {Deep {{Learning}} for {{Visual Tracking}}},
+  author = {Marvasti-Zadeh, Seyed Mojtaba and Cheng, Li and Ghanei-Yakhdan, Hossein and Kasaei, Shohreh},
+  date = {2022-05},
+  journaltitle = IEEE_J_ITS,
+  volume = {23},
+  number = {5},
+  pages = {3943--3968},
+  issn = {1558-0016},
+  doi = {10.1109/TITS.2020.3046478},
+ }

examples/2023botrosRAL.qmd

@@ -0,0 +1,128 @@
+---
+title: "USMicroMagSet: Using Deep Learning Analysis to Benchmark the Performance of Microrobots in Ultrasound Images"
+format:
+  ieee-pdf:
+    keep-tex: true  
+  ieee-html: default
+tex-author-no-affiliation: true
+affiliations:
+  - id: PRISME
+    name: "INSA Centre Val de Loire, Univ. Orléans, PRISME EA4229"
+    affiliation-url: https://www.univ-orleans.fr/fr/prisme
+    city: Bourges
+    country: France
+  - id: pascal
+    name: Clermont Auvergne INP, CNRS, SIGMA Clermont
+    departement: Institut Pascal
+    city: Clermont Ferrand
+    country: France
+    url: http://www.institutpascal.uca.fr    
+author:
+  - name: Karim Botros
+    orcid: 0000-0003-0245-1852
+    membership: Student Member, IEEE
+    affiliations:
+      - ref: PRISME
+    note: 
+      - "Manuscript received 31 October 2022; accepted 9 March 2023. Date of publication 5 April 2023; date of current version 21 April 2023."
+      - "This paper was recommended for publication by Editor Pietro Valdastri upon evaluation of the Associate Editor and Reviewers' comments. This work was supported by the Region Centre Val de Loire Fund with the BUBBLEBOT project."
+  - name: Mohammad Alkhatib
+    affiliations:
+      - ref: pascal
+    orcid: 0000-0003-0971-3835
+    membership: Member, IEEE
+    note: "Karim Botros, David Folio, and Antoine  Ferreira are with the INSA Centre Val de Loire, Laboratoire PRISME, 18000, Bourges, France (e-mail: <karim.botros@insa-cvl.fr>; david.folio@insa-cvl.fr; antoine.ferreira@insacvl.fr)."
+  - name: David Folio
+    affiliations:
+      - ref: PRISME
+    orcid: 0000-0001-9430-6091
+    membership: Member, IEEE
+    email: david.folio@insa-cvl.fr
+    url: https://dfolio.fr/
+    note: "Mohammad Alkhatib is with the Clermont Auvergne INP, CNRS, SIGMA Clermont, Institut Pascal, F-63000 Clermont Ferrand, France"
+  - name: Antoine  Ferreira
+    affiliations:
+      - ref: PRISME
+    orcid: 0000-0001-6295-3876
+    membership: Member, IEEE
+    email: antoine.ferreira@insa-cvl.fr
+    attributes:
+      corresponding: true
+    note: "Digital Object Identifier (DOI): [10.1109/LRA.2023.3264746](https://doi.org/10.1109/LRA.2023.3264746)."
+    
+abstract: |
+  Microscale robots introduce great perspectives into many medical applications such as drug delivery, minimally invasive surgery, and localized biometric diagnostics. Fully automatic microrobots' real-time detection and tracking using medical imagers are actually investigated for future clinical translation. Ultrasound  (US) B-mode imaging has been employed to monitor single agents and collective swarms of microrobots _in vitro_ and _ex vivo_ in controlled experimental conditions. However, low contrast and spatial resolution still limit the effective employment of such a method in a medical microrobotic scenario due to uncertainties associated with the position of microrobots. The positioning error arises due to the inaccuracy of the US-based visual feedback, which is provided by the detection and tracking algorithms. The application of deep learning networks is a promising solution to detect and track real-time microrobots in noisy ultrasonic images. However, what is most striking is the performance gap among state-of-the-art microrobots deep learning detection and tracking research. A key factor of that is the unavailability of large-scale datasets and benchmarks. In this paper, we present the first publicly available B-mode ultrasound dataset for microrobots (_USmicroMagSet_) with accurate annotations which contains more than 40000 samples of magnetic microrobots. 
+  In addition, for analyzing the performance of microrobots included in the proposed benchmark dataset, 4 deep learning detectors and 4 deep learning trackers are used.
+keywords: [Micro/nano robots, Medical robots and systems, deep learning methods.]
+hyphenation: [in-vasive, auto-matic, micro-robot]
+pageheader:
+  left: "IEEE Robotics and Automation Letters. Preprint Version (generated by the authors). Accepted March, 2023"
+  right: 'K. Botros _et al._: USMicroMagSet'
+bibliography: 2023botrosRAL.bib
+date: 2023-04-05
+pdf: 2023botrosRAL.pdf
+funding: Region Centre Val de Loire Fund with the BUBBLEBOT
+citation: 
+  container-title: IEEE Robotics and Automation Letters
+  page: 3254 - 3261
+  type: article-journal
+  publisher: IEEE
+  issued: 2023-06
+  doi: 10.1109/LRA.2023.3264746
+  url: https://ieeexplore.ieee.org/document/10093014
+  volume: 8
+  issue: 6
+---
+
+# Introduction {#sec-intro}
+
+:::{#fig-dataset fig-env="figure*" fig-pos="ht"}
+![](Figures/Figure1.png){width="80%"}
+
+Illustration of the three classes of eight swimming magnetic microrobots used for the $USMicroMagset$ dataset. For each microrobot, 5k images were extracted from 5 videos of 10min each. (Row 1) Class SMF: Steering magnetic field  with three subclasses (sphere, cube and cylinder) of hard NdFeB magnets. (Row 2) Class RMF: Rotating magnetic field with three subclasses (helix microrobot, soft magnetic sheet microrobot, cube microrobot). (Row 3) Class OMF: Oscillating magnetic field with two subclasses (chain-like swimmer, one-armed flagella swimmer with magnetic head).
+:::
+
+[R]{.IEEEPARstart}[ecently]{} untethered mobile magnetic microrobots have been proposed for a wide range of biomedical applications (drug delivery, clot removal, tissue regeneration) @li2017micro due to their small size and ability to operate in confined spaces and hard-to-reach environments @chung2021magnetically. In endovascular navigation, real-time localization and tracking of microrobots are essential for achieving targeted navigation through imaging feedback. To do so, various medical imaging technologies are being used for the detection and tracking of microrobots such as ultrasound (US) imaging, magnetic resonance imaging, computer tomography, infrared fluorescence imaging, X-ray, and positron emission tomography @pane2019imaging. Recently, there has been increasing attention towards the navigation of magnetic microrobots using active mode US imaging endorsed by healthcare applications @scheggi2017magnetic. It can be explained by the fact that ultrasonic imaging is a noninvasive, versatile, low cost and well-established technique that is regularly used in medical settings. 
+The ultrasound B-mode imaging was successfully used at different scales to detect and to track in real-time different robots _in vitro_ and _in vivo_. 
+For example at the millimeter-scale,  soft grippers @khalil2018mechanical, helical swimmers @hu2018smallscale or soft-bodied robots @ackermann2016detection have been investigated. 
+Different tracking techniques have been implemented to track the small-scale robots such as modified Markov chain Monte Carlo data association algorithm @zhang2021kalman, Kalman filtering @zhang2021kalman, and conventional neural networks @tiryaki2022deep. At the microscale, various types of microrobots were successfully demonstrated such as paramagnetic particles @khalil2014magneticbase, acoustically actuated microswimmer @chen2019ultrasound, self-propelled microjets @sanchez2014magnetic and bio-inspired magnetosperm @magdanz2020ironsperm in living tissue. However, the lack of B-mode resolution owing to poor microrobot echogenicity in image contrast, as well as the low signal-to-noise ratio (SNR), hinders the real-time tracking of microrobots in endovascular applications. To enhance spatial resolution, ultrasound phase analysis was implemented in @pane2021realtime to derive microrobot features such as size and position over time allowing to perform imaging and tracking of a low contrast microrobot in chicken breast. 
+Finally, Doppler-based ultrasound appears as a promising tool for tracking microrobots in echogenic and dynamic environments as biological tissues. In [@wang2020realtime; @wang2021ultrasound] a strategy to navigate a nanoparticle microswarm in real-time under ultrasound Doppler imaging guidance for active endovascular delivery was implemented in blood vessels. The Doppler signals near the microswarm in flowing blood environments were observed, and the microswarm was efficiently tracked and navigated in real-time using Doppler feedback. However, taking into the wide diversity of robot geometries and sizes, variability of swimming principles, and robot material echogenicity properties, the proposed state-of-the-art real-time detection and tracking methodologies face shadowing, low contrast, strong attenuation across an image, and fuzziness of vessel boundaries. 
+Microrobot detection and tracking in vessels is a challenging task, particularly when using US imaging. These challenges in US images include speckle noise, dynamic backgrounds, blur, and US image artifacts. This drives the employment of deep learning-based methods to enhance microrobot detection and tracking in vessels @liu2022capsule.
+However, further research on deep learning detection of microrobots in US images is still not fully explored. Furthermore, convolution neural networks (CNN) models and frameworks may also be retrained using a customized dataset, offering deep learning techniques more flexibility than computer vision methods.
+
+
+Moreover, deep learning-driven techniques yield promising  performance for automatic detection and tracking of navigable magnetic microrobots  using ultrasonic imaging @botross2022fully.  But, there is no well-established benchmarking dataset. Typically, some research teams test and report the performance of their technique on their own private dataset utilizing experimental setups designed expressly for that purpose. Consequently, comparing the performance of multiple approaches or predicting how a given methodology will perform if the experimental setup/conditions change is challenging.
+ 
+In this work, we propose a public large-scale benchmarking dataset ($USMicroMagset$),  summarized in  @fig-dataset. The dataset consists of ultrasound B-mode images of magnetic microrobots with different aspect ratios, sizes (1 mm to 300 µm), shapes, soft/rigid structures, and locomotion principles. This survey provides some insights into the challenges of detectability and trackability of a wide variety of magnetic microrobots navigating in microfluidic channels mimicking a vessel network.
+In addition, this dataset is used to perform a comprehensive survey of deep learning-based microrobot detection and tracking in ultrasonic B-mode images. Based on these comparative analyses [@srivastava2021comparative; @marvasti-zadeh2022deep], we have extracted the best four detectors and the best four trackers. Then we evaluated these detectors and trackers on our dataset $USMicroMagset$. Finally, using the best detector and tracker algorithms, we evaluated each microrobot under real-time challenging scenarios simulating in vivo imaging problems such as changing ultrasound parameters, changing speed of microrobot motion, partial and full occlusion, and out-of-plane motion.
+
+The paper is organized as follows: @sec-benchmarking describes the datasets and the methods used in the proposed work. Section 3 describes the deep learning detectors and trackers. Section 4 made a comparative assessment of the different detectors and trackers before concluding.
+
+# Microrobot Benchmarking {#sec-benchmarking}
+
+##  Classes
+
+In the proposed $USMicroMagset$ dataset, there are three different classes describing three operating principles of hard and soft magnetic microrobots. Our dataset is composed of 40k images  subdivided into three classes of magnetic microrobots.
+@fig-dataset summarizes the locomotion principles for each class of magnetic microrobot used in the datasets. We recorded 5k images for each microrobot prototype (80\% for training and 20\% for testing). All images have been annotated manually by an experienced expert.
+
+## Microrobot principle
+Basically, governing equations of magnetized hard and soft microrobots controlled by magnetic fields are as follows :
+\begin{eqnarray}
+{\vb{\textbf{F}}_{m}}&= \left(\vb{M}\cdot \nabla\right) \vb{B} \label{eq:Fm} \\
+\vb{\textbf{T}_{m}}&= \left(\vb{M}\times\vb{B}\right) \label{eq:Tm} 
+\end{eqnarray}
+where $\vb{\textbf{T}_{m}}$ is the resultant magnetic torque; $\vb{\textbf{F}_{m}}$ is the resultant magnetic force; $\vb{M}$ is the magnetic moment of the object; $\vb{B}$ is the applied magnetic field. Depending on the actuating magnetic fields, three classes have been identified and tested:
+
+### Steering magnetic field (SMF) class
+The first class consists of three subclasses of force-driven microrobots (Row\;1 in @fig-dataset) using various shapes of hard NdFeB magnets (spheres, cubes, and cylinders). 
+The steering motion relies on the use of SMF induced by three-axis Maxwell pair coils.
+
+The resultant steering force $\vb{\textbf{F}_{m}}$ provides a linear steering motion of the microrobot [@kummer2010octomag; @mathieu2010steeringa; @ghanbari2014electromagnetic]
+
+:::{.center data-latex=""}
+**Full text are available from [IEEEXplore^®^](https://ieeexplore.ieee.org/document/10093014/)**
+:::
+
+::: {.content-visible when-format="pdf"}
+# References {-}
+:::