{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T02:53:43Z","timestamp":1772247223132,"version":"3.50.1"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030804312","type":"print"},{"value":"9783030804329","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-80432-9_2","type":"book-chapter","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T23:08:25Z","timestamp":1625526505000},"page":"18-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["An Efficient One-Stage Detector for Real-Time Surgical Tools Detection in Robot-Assisted Surgery"],"prefix":"10.1007","author":[{"given":"Yu","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijian","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanyuan","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Choi, B., Jo, K., Choi, S., Choi, J.: Surgical-tools detection based on convolutional neural network in laparoscopic robot-assisted surgery. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), vol. 2017, pp. 1756\u20131759 (2017)","DOI":"10.1109\/EMBC.2017.8037183"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhao, Z., Chang, F., Hu, S.: An anchor-free convolutional neural network for real-time surgical tool detection in robot-assisted surgery. IEEE Access. PP(99), 1 (2020)","DOI":"10.1109\/ACCESS.2020.2989807"},{"issue":"1","key":"2_CR3","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1109\/TMI.2016.2593957","volume":"36","author":"AP Twinanda","year":"2017","unstructured":"Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., De Mathelin, M., Padoy, N.: Endonet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86\u201397 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.media.2018.05.001","volume":"47","author":"HA Hajj","year":"2018","unstructured":"Hajj, H.A., Lamard, M., Conze, P.H., Cochener, B., Quellec, G.: Monitoring tool usage in surgery videos using boosted convolutional and recurrent neural networks. Med. Image Anal. 47, 203\u2013218 (2018)","journal-title":"Med. Image Anal."},{"issue":"1","key":"2_CR5","first-page":"33","volume":"5","author":"M Sahu","year":"2016","unstructured":"Sahu, M., Moerman, D., Mewes, P., Mountney, P., Rose, G.: Instrument state recognition and tracking for effective control of robotized laparoscopic systems. Int. J. Mech. Eng. Robot. Res. 5(1), 33\u201338 (2016)","journal-title":"Int. J. Mech. Eng. Robot. Res."},{"issue":"6","key":"2_CR6","doi-asserted-by":"publisher","first-page":"1109","DOI":"10.1007\/s11548-016-1393-4","volume":"11","author":"X Du","year":"2016","unstructured":"Du, X., et al.: Combined 2D and 3D tracking of surgical instruments for minimally invasive and robotic-assisted surgery. Int. J. Comput. Assist. Radiol. Surg. 11(6), 1109\u20131119 (2016)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"14","key":"2_CR7","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1049\/joe.2018.9401","volume":"2019","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Voros, S., Chen, Z., Cheng, X.: Surgical tool tracking based on two CNNs: from coarse to fine. The J. Eng. 2019(14), 467\u2013472 (2019)","journal-title":"The J. Eng."},{"issue":"6","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1007\/s11548-019-01958-6","volume":"14","author":"CI Nwoye","year":"2019","unstructured":"Nwoye, C.I., Mutter, D., Marescaux, J., Padoy, N.: Weakly supervised convolutional lstm approach for tool tracking in laparoscopic videos. Int. J. Comput. Assist. Radiol. Surg. 14(6), 1059\u20131067 (2019)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Garc\u00b4l\u0142a-Peraza-Herrera, L.C., et al.: Real-time segmentation of non-rigid surgical tools based on deep learning and tracking. In: International Workshop on Computer-Assisted and Robotic Endoscopy (2016)","DOI":"10.1007\/978-3-319-54057-3_8"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.91"},{"key":"2_CR11","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection (2020)"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Shi, P., Zhao, Z., Hu, S., et al.: Real-time surgical tool detection in minimally invasive surgery based on attention-guided convolutional neural network. IEEE Access PP(99), 1\u20131 (2020)","DOI":"10.1109\/ACCESS.2020.3046258"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional neural networks. In: European Conference on Computer Vision (2013)","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C.: Ghostnet: more features from cheap operations. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: SSD: single shot multibox detector (2016)","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Liao, H.Y.M., Wu, Y.H., Chen, P.Y., Yeh, I.H.: CSPNet: a new backbone that can enhance learning capability of CNN. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2020)","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"2_CR17","unstructured":"Misra, D.: Mish: a self regularized non-monotonic activation function (2019)"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"9","key":"2_CR19","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2014","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904\u20131916 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR20","unstructured":"Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv e-prints (2018)"},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ren, D.: Distance-IoU loss: faster and better learning for bounding box regression. In: AAAI Conference on Artificial Intelligence (2020)","DOI":"10.1609\/aaai.v34i07.6999"},{"issue":"5","key":"2_CR22","doi-asserted-by":"publisher","first-page":"1276","DOI":"10.1109\/TMI.2017.2787672","volume":"37","author":"X Du","year":"2018","unstructured":"Du, X., et al.: Articulated multi-instrument 2-d pose estimation using fully convolutional networks. IEEE Trans. Med. Imaging 37(5), 1276\u20131287 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Neubeck, A., Gool, L.J.V.: Efficient Non-Maximum Suppression (2006)","DOI":"10.1109\/ICPR.2006.479"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J.Y., Sadeghian, A., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00075"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster r-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2016)","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"2_CR26","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Understanding and Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-80432-9_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T17:30:08Z","timestamp":1710264608000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-80432-9_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030804312","9783030804329"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-80432-9_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIUA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Annual Conference on Medical Image Understanding and Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Oxford","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miua2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miua2021.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"77","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"42% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,8","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}