{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T01:59:36Z","timestamp":1780365576057,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031755392","type":"print"},{"value":"9783031755408","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T00:00:00Z","timestamp":1729123200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-75540-8_17","type":"book-chapter","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T23:03:34Z","timestamp":1729119814000},"page":"222-235","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploring Anchor-Free Object Detection Models for Surgical Tool Detection: A Comparative Study of Faster-RCNN, YOLOv4, and CenterNet++"],"prefix":"10.1007","author":[{"given":"Carlos","family":"Aparicio","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuauht\u00e9moc","family":"Guerrero","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mansoor","family":"Ali Teevno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gilberto","family":"Ochoa-Ruiz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharib","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,17]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.wneu.2022.03.121","volume":"163","author":"JL Goldberg","year":"2022","unstructured":"Goldberg, J.L., Hussain, I., Sommer, F., H\u00e4rtl, R., Elowitz, E.: The future of minimally invasive spinal surgery. W. Neurosurg. 163, 233\u2013240 (2022). https:\/\/doi.org\/10.1016\/j.wneu.2022.03.121","journal-title":"W. Neurosurg."},{"issue":"4","key":"17_CR2","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1007\/s12055-023-01501-y","volume":"39","author":"J Claessens","year":"2023","unstructured":"Claessens, J., Rottiers, R., Vandenbrande, J., Gruyters, I., Yilmaz, A., Kaya, A., Stessel, B.: Quality of life in patients undergoing minimally invasive cardiac surgery: a systematic review. Indian J. Thorac. Cardiovasc. Surg. 39(4), 367\u2013380 (2023). https:\/\/doi.org\/10.1007\/s12055-023-01501-y","journal-title":"Indian J. Thorac. Cardiovasc. Surg."},{"issue":"3","key":"17_CR3","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1097\/BSD.0000000000000618","volume":"31","author":"AS Narain","year":"2018","unstructured":"Narain, A.S., Hijji, F.Y., Duhancioglu, G., Haws, B.E., Khechen, B., Manning, B.T., Colman, M.W., Singh, K.: Patient perceptions of minimally invasive versus open spine surgery. Clin. Spine. Surg. 31(3), 184\u2013192 (2018). https:\/\/doi.org\/10.1097\/BSD.0000000000000618","journal-title":"Clin. Spine. Surg."},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Rudiman, R.: Minimally invasive gastrointestinal surgery: from past to the future. Ann. Med. Surg. (Lond) 71, 102922 (2021) https:\/\/doi.org\/10.1016\/j.amsu.2021.102922","DOI":"10.1016\/j.amsu.2021.102922"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Ka\u00e7maz, E., Engelsman, A.F., Bemelman, W.A., Tanis, P.J., Dijkum, E.J.M., Surgery\u00a0(ISGSS), I.S.G.: International survey on opinions and use of minimally invasive surgery in small bowel neuroendocrine neoplasms. Eur. J. Surg. Oncol. 48(6), 1251\u20131257 (2022) https:\/\/doi.org\/10.1016\/j.ejso.2021.11.011","DOI":"10.1016\/j.ejso.2021.11.011"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Wang, Y., Sun, Q., Liu, Z., Gu, L.: Visual detection and tracking algorithms for minimally invasive surgical instruments: A comprehensive review of the state-of-the-art. Robotics and Autonomous Systems 149, 103945 (2022) https:\/\/doi.org\/10.1016\/j.robot.2021.103945","DOI":"10.1016\/j.robot.2021.103945"},{"issue":"S1","key":"17_CR7","doi-asserted-by":"publisher","first-page":"81","DOI":"10.3233\/THC-209009","volume":"28","author":"T Cai","year":"2020","unstructured":"Cai, T., Zhao, Z.: Convolutional neural network-based surgical instrument detection. Technol. Health Care 28(S1), 81\u201388 (2020). https:\/\/doi.org\/10.3233\/THC-209009","journal-title":"Technol. Health Care"},{"key":"17_CR8","doi-asserted-by":"publisher","unstructured":"Zhang, B., Wang, S., Dong, L., Chen, P.: Surgical tools detection based on modulated anchoring network in laparoscopic videos. IEEE Access 8, 23748\u201323758 (2020) https:\/\/doi.org\/10.1109\/ACCESS.2020.2969885","DOI":"10.1109\/ACCESS.2020.2969885"},{"issue":"4","key":"17_CR9","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1109\/TMRB.2023.3310031","volume":"5","author":"W Wang","year":"2023","unstructured":"Wang, W., Luo, Y., Wang, J., Wang, X., Song, H.: Toolnet-x: surgical instrument detection combined with high-order spatial interaction. IEEE Trans. Med. Robot. Bion. 5(4), 857\u2013866 (2023). https:\/\/doi.org\/10.1109\/TMRB.2023.3310031","journal-title":"IEEE Trans. Med. Robot. Bion."},{"key":"17_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-024-03115-0","author":"X Pan","year":"2024","unstructured":"Pan, X., Bi, M., Wang, H., Ma, C., He, X.: Dbh-yolo: a surgical instrument detection method based on feature separation in laparoscopic surgery. Int. J. Comput. Assist. Radiol. Surg. (2024). https:\/\/doi.org\/10.1007\/s11548-024-03115-0","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"17_CR11","doi-asserted-by":"publisher","unstructured":"Ran, B., Huang, B., Liang, S., Hou, Y.: Surgical instrument detection algorithm based on improved yolov7x. Sensors 23(11) (2023) https:\/\/doi.org\/10.3390\/s23115037","DOI":"10.3390\/s23115037"},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Liu, Z., Zhou, Y., Zheng, L., Zhang, G.: Sinet: a hybrid deep cnn model for real-time detection and segmentation of surgical instruments. Biomed. Signal Process. Control 88, 105670 (2024) https:\/\/doi.org\/10.1016\/j.bspc.2023.105670","DOI":"10.1016\/j.bspc.2023.105670"},{"issue":"3","key":"17_CR13","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1007\/s11263-019-01204-1","volume":"128","author":"H Law","year":"2020","unstructured":"Law, H., Deng, J.: Cornernet: Detecting objects as paired keypoints. Int. J. Comput. Vision 128(3), 642\u2013656 (2020). https:\/\/doi.org\/10.1007\/s11263-019-01204-1","journal-title":"Int. J. Comput. Vision"},{"key":"17_CR14","unstructured":"Zhou, X., Wang, D., Kr\u00e4henb\u00fchl, P.: Objects as Points (2019)"},{"key":"17_CR15","doi-asserted-by":"publisher","unstructured":"Tian, Z., Shen, C., Chen, H., He, T.: Fcos: fully convolutional one-stage object detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9626\u20139635 (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"17_CR16","doi-asserted-by":"publisher","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 8, 78193\u201378201 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2989807","DOI":"10.1109\/ACCESS.2020.2989807"},{"issue":"05","key":"17_CR17","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.1109\/TPAMI.2023.3342120","volume":"46","author":"K Duan","year":"2024","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., Tian, Q.: Centernet++ for object detection. IEEE Trans. Pattern Anal. & Mach. Intell. 46(05), 3509\u20133521 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2023.3342120","journal-title":"IEEE Trans. Pattern Anal. & Mach. Intell."},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Jin, A., Yeung, S., Jopling, J., Krause, J., Azagury, D., Milstein, A., Fei-Fei, L.: Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks. IEEE Winter Conf. Appl. Comput. Vision (2018)","DOI":"10.1109\/WACV.2018.00081"},{"key":"17_CR19","unstructured":"Ren, S., He, K., Girshick, R.B., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28 (NeurIPS), pp. 91\u201399 (2015). https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/14bfa6bb14875e45bba028a21ed38046-Paper.pdf"},{"key":"17_CR20","unstructured":"Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-75540-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T23:22:12Z","timestamp":1729120932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-75540-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,17]]},"ISBN":["9783031755392","9783031755408"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-75540-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,17]]},"assertion":[{"value":"17 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tonantzintla","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.micai.org\/2024\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}