{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T00:21:36Z","timestamp":1726273296966},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:00:00Z","timestamp":1725062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T00:00:00Z","timestamp":1725062400000},"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":["SIViP"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1007\/s11760-024-03451-3","type":"journal-article","created":{"date-parts":[[2024,8,31]],"date-time":"2024-08-31T18:02:01Z","timestamp":1725127321000},"page":"8061-8076","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transfer learning for surgical instrument segmentation in open surgery videos: a modified u-net approach with channel amplification"],"prefix":"10.1007","volume":"18","author":[{"given":"K.","family":"Bakiya","sequence":"first","affiliation":[]},{"given":"Nickolas","family":"Savarimuthu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,31]]},"reference":[{"issue":"3","key":"3451_CR1","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1109\/TMI.2021.3121138","volume":"41","author":"J Liu","year":"2022","unstructured":"Liu, J., Guo, X., Yuan, Y.: Graph-based surgical instrument adaptive segmentation via domain-common knowledge. IEEE Trans. Med. Imaging 41(3), 715\u2013726 (2022). https:\/\/doi.org\/10.1109\/TMI.2021.3121138","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3451_CR2","doi-asserted-by":"publisher","first-page":"122627","DOI":"10.1109\/ACCESS.2022.3223704","volume":"10","author":"SM Hussain","year":"2022","unstructured":"Hussain, S.M., Brunetti, A., Lucarelli, G., Memeo, R., Bevilacqua, V., Buongiorno, D.: Deep learning based image processing for robot assisted surgery: a systematic literature survey. IEEE Access 10, 122627\u2013122657 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3223704","journal-title":"IEEE Access"},{"key":"3451_CR3","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1007\/s41315-020-00127-2","volume":"4","author":"L Qiu","year":"2020","unstructured":"Qiu, L., Ren, H.: Endoscope navigation with SLAM-based registration to computed tomography for transoral surgery. Int. J. Intell. Robot Appl. 4, 252\u2013263 (2020). https:\/\/doi.org\/10.1007\/s41315-020-00127-2","journal-title":"Int. J. Intell. Robot Appl."},{"key":"3451_CR4","volume-title":"Handbook of robotic and image-guided surgery","author":"H Ren","year":"2020","unstructured":"Ren, H., Li, C., Qiu, L., Lim, C Ming: 38-ACTORS: adaptive and compliant transoral robotic surgery with flexible manipulators and intelligent guidance. In: Abedin-Nasab, H.H. (ed.) Handbook of robotic and image-guided surgery. Elsevier, New Jersey (2020)"},{"key":"3451_CR5","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1007\/s40846-019-00475-w","volume":"39","author":"AK Srivastava","year":"2019","unstructured":"Srivastava, A.K., Singhvi, S., Qiu, L., et al.: Image guided navigation utilizing intra-operative 3D surface scanning to mitigate morphological deformation of surface anatomy. J. Med. Biol. Eng. 39, 932\u2013943 (2019). https:\/\/doi.org\/10.1007\/s40846-019-00475-w","journal-title":"J. Med. Biol. Eng."},{"issue":"4","key":"3451_CR6","doi-asserted-by":"publisher","first-page":"2655","DOI":"10.1109\/TASE.2022.3203631","volume":"20","author":"H Gao","year":"2023","unstructured":"Gao, H., et al.: SAVAnet: surgical action-driven visual attention network for autonomous endoscope control. IEEE Trans. Autom. Sci. Eng. 20(4), 2655\u20132667 (2023). https:\/\/doi.org\/10.1109\/TASE.2022.3203631","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"3451_CR7","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1007\/s10489-022-03642-w","volume":"53","author":"T Dhamija","year":"2023","unstructured":"Dhamija, T., Gupta, A., Gupta, S., et al.: Semantic segmentation in medical images through transfused convolution and transformer networks. Appl. Intell. 53, 1132\u20131148 (2023). https:\/\/doi.org\/10.1007\/s10489-022-03642-w","journal-title":"Appl. Intell."},{"issue":"12","key":"3451_CR8","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Sandler M et al.: (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: proceedings of the IEEE conference on computer vision and pattern recognition","key":"#cr-split#-3451_CR9.1","DOI":"10.1109\/CVPR.2018.00474"},{"unstructured":"piscataway: IEEE, pp. 4510-4520. (2018)","key":"#cr-split#-3451_CR9.2"},{"doi-asserted-by":"publisher","unstructured":"Liu, Z et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF international conference on computer vision (ICCV), Montreal, QC, Canada, pp. 9992\u201310002, (2021) https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986.","key":"3451_CR10","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"2","key":"3451_CR11","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1109\/TMRB.2022.3170215","volume":"4","author":"B Huang","year":"2022","unstructured":"Huang, B., et al.: Simultaneous depth estimation and surgical tool segmentation in laparoscopic images. IEEE Trans. Med. Robot. Bionic. 4(2), 335\u2013338 (2022). https:\/\/doi.org\/10.1109\/TMRB.2022.3170215","journal-title":"IEEE Trans. Med. Robot. Bionic."},{"issue":"2","key":"3451_CR12","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1109\/TMRB.2023.3269856","volume":"5","author":"L Yang","year":"2023","unstructured":"Yang, L., Wang, H., Gu, Y., Bian, G., Liu, Y., Yu, H.: TMA-Net: A Transformer-based multi-scale attention network for surgical instrument segmentation. IEEE Trans. Med. Robot. Bionic. 5(2), 323\u2013334 (2023). https:\/\/doi.org\/10.1109\/TMRB.2023.3269856","journal-title":"IEEE Trans. Med. Robot. Bionic."},{"issue":"10","key":"3451_CR13","doi-asserted-by":"publisher","first-page":"2832","DOI":"10.1109\/TMI.2023.3266137","volume":"42","author":"A Lou","year":"2023","unstructured":"Lou, A., Tawfik, K., Yao, X., Liu, Z., Noble, J.: Min-max similarity: a contrastive semi-supervised deep learning network for surgical tools segmentation. IEEE Trans. Med. Imaging 42(10), 2832\u20132841 (2023). https:\/\/doi.org\/10.1109\/TMI.2023.3266137","journal-title":"IEEE Trans. Med. Imaging"},{"key":"3451_CR14","doi-asserted-by":"publisher","first-page":"1607","DOI":"10.1007\/s11548-021-02438-6","volume":"16","author":"X Kong","year":"2021","unstructured":"Kong, X., Jin, Y., Dou, Q., et al.: Accurate instance segmentation of surgical instruments in robotic surgery: model refinement and cross-dataset evaluation. Int J CARS 16, 1607\u20131614 (2021). https:\/\/doi.org\/10.1007\/s11548-021-02438-6","journal-title":"Int J CARS"},{"unstructured":"Allan, M., Shvets, A., Kurmann, T., Zhang, Z., Duggal, R., Su, Y. et al.: 2017 robotic instrument segmentation challenge. arXiv preprint arXiv:1902.06426 (2019)","key":"3451_CR15"},{"issue":"12","key":"3451_CR16","doi-asserted-by":"publisher","first-page":"2603","DOI":"10.1109\/TMI.2015.2450831","volume":"34","author":"D Bouget","year":"2015","unstructured":"Bouget, D., Benenson, R., Omran, M., Riffaud, L., Schiele, B., Jannin, P.: Detecting surgical tools by modelling local appearance and global shape. IEEE Trans. Med. Imaging 34(12), 2603\u20132617 (2015). https:\/\/doi.org\/10.1109\/TMI.2015.2450831","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"3451_CR17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1145\/3391743","volume":"11","author":"R Yao","year":"2020","unstructured":"Yao, R., Lin, G., Xia, S., Zhao, J., Zhou, Y.: Video object segmentation and tracking: a survey. ACM Trans. Intell. Syst. Technol. 11(4), 47 (2020). https:\/\/doi.org\/10.1145\/3391743","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"6","key":"3451_CR18","doi-asserted-by":"publisher","first-page":"7099","DOI":"10.1109\/TPAMI.2022.3225573","volume":"45","author":"T Zhou","year":"2023","unstructured":"Zhou, T., Porikli, F., Crandall, D.J., Van Gool, L., Wang, W.: A survey on deep learning technique for video segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 7099\u20137122 (2023). https:\/\/doi.org\/10.1109\/TPAMI.2022.3225573","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3451_CR19","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1049\/htl.2019.0068","volume":"6","author":"L Qiu","year":"2019","unstructured":"Qiu, L., Li, C., Ren, H.: Real-time surgical instrument tracking in robot-assisted surgery using multi-domain convolutional neural network. Healthc. Technol. Lett. 6, 159\u2013164 (2019). https:\/\/doi.org\/10.1049\/htl.2019.0068","journal-title":"Healthc. Technol. Lett."},{"issue":"1","key":"3451_CR20","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1109\/TASE.2014.2343652","volume":"12","author":"C Nadeau","year":"2015","unstructured":"Nadeau, C., Ren, H., Krupa, A., Dupont, P.: Intensity-based visual servoing for instrument and tissue tracking in 3D ultrasound volumes. IEEE Trans. Autom. Sci. Eng. 12(1), 367\u2013371 (2015). https:\/\/doi.org\/10.1109\/TASE.2014.2343652","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"5","key":"3451_CR21","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). https:\/\/doi.org\/10.1109\/TMI.2017.2787672","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"3451_CR22","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1109\/TMI.2018.2794439","volume":"37","author":"M Allan","year":"2018","unstructured":"Allan, M., Ourselin, S., Hawkes, D.J., Kelly, J.D., Stoyanov, D.: 3-D Pose estimation of articulated instruments in robotic minimally invasive surgery. IEEE Trans. Med. Imaging 37(5), 1204\u20131213 (2018). https:\/\/doi.org\/10.1109\/TMI.2018.2794439","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"3451_CR23","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/LRA.2019.2900854","volume":"4","author":"M Islam","year":"2019","unstructured":"Islam, M., Atputharuban, D.A., Ramesh, R., Ren, H.: Real-Time instrument segmentation in robotic surgery using auxiliary supervised deep adversarial learning. IEEE Robot. Autom. Lett. 4(2), 2188\u20132195 (2019). https:\/\/doi.org\/10.1109\/LRA.2019.2900854","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3451_CR24","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10278-019-00277-1","volume":"33","author":"L Yu","year":"2020","unstructured":"Yu, L., Wang, P., Yu, X., et al.: A holistically-nested U-Net: surgical instrument segmentation based on convolutional neural network. J. Digit. Imaging 33, 341\u2013347 (2020). https:\/\/doi.org\/10.1007\/s10278-019-00277-1","journal-title":"J. Digit. Imaging"},{"issue":"3","key":"3451_CR25","doi-asserted-by":"publisher","first-page":"129","DOI":"10.3109\/13645706.2011.580764","volume":"21","author":"L Bouarfa","year":"2012","unstructured":"Bouarfa, L., Akman, O., Schneider, A., Jonker, P.P., Dankelman, J.: In-vivo real-time tracking of surgical instruments in endoscopic video. Minim. Invasive Ther. Allied Technol. 21(3), 129\u2013134 (2012). https:\/\/doi.org\/10.3109\/13645706.2011.580764","journal-title":"Minim. Invasive Ther. Allied Technol."},{"issue":"3","key":"3451_CR26","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1109\/TASE.2021.3087868","volume":"19","author":"L Qiu","year":"2022","unstructured":"Qiu, L., Ren, H.: RSegNet: a joint learning framework for deformable registration and segmentation. IEEE Trans. Autom. Sci. Eng. 19(3), 2499\u20132513 (2022). https:\/\/doi.org\/10.1109\/TASE.2021.3087868","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"3451_CR27","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1007\/s11548-021-02383-4","volume":"16","author":"M Sahu","year":"2021","unstructured":"Sahu, M., Mukhopadhyay, A., Zachow, S.: Simulation-to-real domain adaptation with teacher\u2013student learning for endoscopic instrument segmentation. Int J CARS 16, 849\u2013859 (2021). https:\/\/doi.org\/10.1007\/s11548-021-02383-4","journal-title":"Int J CARS"},{"issue":"3","key":"3451_CR28","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TMRB.2022.3193420","volume":"4","author":"L Yang","year":"2022","unstructured":"Yang, L., Gu, Y., Bian, G., Liu, Y.: DRR-Net: a dense-connected residual recurrent convolutional network for surgical instrument segmentation from endoscopic images. IEEE Trans. Med. Robot. Bion. 4(3), 696\u2013707 (2022). https:\/\/doi.org\/10.1109\/TMRB.2022.3193420","journal-title":"IEEE Trans. Med. Robot. Bion."},{"issue":"11","key":"3451_CR29","doi-asserted-by":"publisher","first-page":"7202","DOI":"10.1109\/LRA.2023.3315229","volume":"8","author":"L Wang","year":"2023","unstructured":"Wang, L., Zhou, C., Cao, Y., Zhao, R., Xu, K.: Vision-based markerless tracking for continuum surgical instruments in robot-assisted minimally invasive surgery. IEEE Robot. Autom. Lett. 8(11), 7202\u20137209 (2023). https:\/\/doi.org\/10.1109\/LRA.2023.3315229","journal-title":"IEEE Robot. Autom. Lett."},{"issue":"sup1","key":"3451_CR30","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/24699322.2018.1560097","volume":"24","author":"Z Zhao","year":"2019","unstructured":"Zhao, Z., Chen, Z., Voros, S., Cheng, X.: Real-time tracking of surgical instruments based on spatio-temporal context and deep learning. Comput. Assist. Surg. 24(sup1), 20\u201329 (2019). https:\/\/doi.org\/10.1080\/24699322.2018.1560097","journal-title":"Comput. Assist. Surg."},{"issue":"4","key":"3451_CR31","doi-asserted-by":"publisher","first-page":"6773","DOI":"10.1109\/LRA.2021.3096156","volume":"6","author":"S Lin","year":"2021","unstructured":"Lin, S., Qin, F., Peng, H., Bly, R.A., Moe, K.S., Hannaford, B.: Multi-frame feature aggregation for real-time instrument segmentation in endoscopic video. IEEE Robot. Autom. Lett. 6(4), 6773\u20136780 (2021). https:\/\/doi.org\/10.1109\/LRA.2021.3096156","journal-title":"IEEE Robot. Autom. Lett."},{"key":"3451_CR32","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1049\/ipr2.12283","volume":"15","author":"X Wang","year":"2021","unstructured":"Wang, X., et al.: PaI-Net: a modified u-net of reducing semantic gap for surgical instrument segmentation. IET Image Process. 15, 2959\u20132969 (2021). https:\/\/doi.org\/10.1049\/ipr2.12283","journal-title":"IET Image Process."},{"key":"3451_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3225922","volume":"72","author":"L Yang","year":"2023","unstructured":"Yang, L., Gu, Y., Bian, G., Liu, Y.: TMF-Net: a transformer-based multiscale fusion network for surgical instrument segmentation from endoscopic images. IEEE Trans. Instrum. Meas. 72, 1\u201315 (2023). https:\/\/doi.org\/10.1109\/TIM.2022.3225922","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"0n203","key":"3451_CR34","doi-asserted-by":"publisher","first-page":"2241003","DOI":"10.1142\/S2424905X22410033","volume":"07","author":"K Huang","year":"2022","unstructured":"Huang, K., Chitrakar, D., Jiang, W., Yung, I., Yun-Hsuan, S.: Surgical tool segmentation with pose-informed morphological polar transform of endoscopic images. J. Med. Robot. Res. 07(0n203), 2241003 (2022). https:\/\/doi.org\/10.1142\/S2424905X22410033","journal-title":"J. Med. Robot. Res."},{"key":"3451_CR35","doi-asserted-by":"publisher","first-page":"102310","DOI":"10.1016\/j.media.2021.102310","volume":"76","author":"Z-L Ni","year":"2022","unstructured":"Ni, Z.-L., Zhou, X.-H., Wang, G.-A., Yue, W.-Q., Li, Z., Bian, G.-B., Hou, Z.-G.: SurgiNet: pyramid attention aggregation and class-wise self-distillation for surgical instrument segmentation. Med. Image Anal. 76, 102310 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102310","journal-title":"Med. Image Anal."},{"key":"3451_CR36","doi-asserted-by":"publisher","first-page":"102569","DOI":"10.1016\/j.media.2022.102569","volume":"81","author":"JC\u00c1 Cer\u00f3n","year":"2022","unstructured":"Cer\u00f3n, J.C.\u00c1., Ruiz, G.O., Chang, L., Ali, S.: Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion. Med. Image Anal. 81, 102569 (2022). https:\/\/doi.org\/10.1016\/j.media.2022.102569","journal-title":"Med. Image Anal."},{"issue":"7","key":"3451_CR37","doi-asserted-by":"publisher","first-page":"3209","DOI":"10.1109\/JBHI.2022.3154925","volume":"26","author":"Z-L Ni","year":"2022","unstructured":"Ni, Z.-L., Bian, G.-B., Li, Z., Zhou, X.-H., Li, R.-Q., Hou, Z.-G.: Space squeeze reasoning and low-rank bilinear feature fusion for surgical image segmentation. IEEE J. Biomed. Health Inform. 26(7), 3209\u20133217 (2022). https:\/\/doi.org\/10.1109\/JBHI.2022.3154925","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"3451_CR38","doi-asserted-by":"publisher","first-page":"106216","DOI":"10.1016\/j.compbiomed.2022.106216","volume":"151","author":"L Yang","year":"2022","unstructured":"Yang, L., Yuge, G., Bian, G., Liu, Y.: An attention-guided network for surgical instrument segmentation from endoscopic images. Comput. Biol. Med. 151, 106216 (2022). https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106216","journal-title":"Comput. Biol. Med."},{"key":"3451_CR39","doi-asserted-by":"publisher","first-page":"104912","DOI":"10.1016\/j.bspc.2023.104912","volume":"85","author":"L Yang","year":"2023","unstructured":"Yang, L., Yuge, G., Bian, G., Liu, Y.: MAF-Net: a multi-scale attention fusion network for automatic surgical instrument segmentation. Biomed. Signal Proc. Control 85, 104912 (2023). https:\/\/doi.org\/10.1016\/j.bspc.2023.104912","journal-title":"Biomed. Signal Proc. Control"},{"issue":"4","key":"3451_CR40","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/TMRB.2023.3315479","volume":"5","author":"L Yang","year":"2023","unstructured":"Yang, L., Wang, H., Bian, G., Liu, Y.: HCTA-Net: a hybrid CNN-transformer attention network for surgical instrument segmentation. IEEE Trans. Med. Robot. Bion. 5(4), 929\u2013944 (2023). https:\/\/doi.org\/10.1109\/TMRB.2023.3315479","journal-title":"IEEE Trans. Med. Robot. Bion."},{"key":"3451_CR41","doi-asserted-by":"publisher","first-page":"43837","DOI":"10.1007\/s11042-022-13215-1","volume":"81","author":"MT Nyo","year":"2022","unstructured":"Nyo, M.T., Mebarek-Oudina, F., Hlaing, S.S., Khan, N.A.: Otsu\u2019s thresholding technique for MRI image brain tumor segmentation. Multimed. Tools Appl. 81, 43837\u201343849 (2022). https:\/\/doi.org\/10.1007\/s11042-022-13215-1","journal-title":"Multimed. Tools Appl."},{"key":"3451_CR42","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/j.eswa.2018.09.008","volume":"116","author":"MH Merzban","year":"2019","unstructured":"Merzban, M.H., Elbayoumi, M.: Efficient solution of Otsu multilevel image thresholding: A comparative study. Expert Syst. Appl. 116, 299\u2013309 (2019). https:\/\/doi.org\/10.1016\/j.eswa.2018.09.008","journal-title":"Expert Syst. Appl."},{"unstructured":"https:\/\/endovissub2017-roboticinstrumentsegmentation.grandchallenge.org","key":"3451_CR43"},{"doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. In Proc. Int. Conf. Med. image comput. comput.-assist. intervent. (MICCAI). Cham, Switzerland: Springer, pp. 234\u2013241. (2015)","key":"3451_CR44","DOI":"10.1007\/978-3-319-24574-4_28"},{"unstructured":"https:\/\/medicis.univ-rennes1.fr\/software","key":"3451_CR45"},{"unstructured":"Iglovikov, V., Shvets, A.: TernausNet: U-Net with VGG11encoder pre-trained on ImageNet for image segmentation. (2018). arXiv:1801.05746","key":"3451_CR46"},{"unstructured":"Oktay, O. et al.: Attention U-Net: Learning where to look for the pancreas. (2018). arXiv:1804.03999","key":"3451_CR47"},{"unstructured":"Hasan, S. M. K., Linte, C. A.: U-NetPlus: A modified encoder\u2013decoder U-Net architecture for semantic and instance segmentation of surgical instruments from laparoscopic images. In Proc. 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), pp. 7205\u20137211. (2019)","key":"3451_CR48"},{"doi-asserted-by":"crossref","unstructured":"Y. Jin, Y., Cheng, K., Dou, Q., Heng, P-A.: Incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video. In Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI). Cham, Switzerland: Springer, pp. 440\u2013448. (2019)","key":"3451_CR49","DOI":"10.1007\/978-3-030-32254-0_49"},{"doi-asserted-by":"crossref","unstructured":"Ni, Z-L., Bian, G-B., Xie, X-L., Hou, Z-G., Zhou, X-H., Zhou, Y-J.: RASNet: Segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network. In Proc. 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC), pp. 5735\u20135738. (2019)","key":"3451_CR50","DOI":"10.1109\/EMBC.2019.8856495"},{"issue":"6","key":"3451_CR51","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: UNet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imag. 39(6), 1856\u20131867 (2020)","journal-title":"IEEE Trans. Med. Imag."},{"doi-asserted-by":"crossref","unstructured":"Liu, D., et al.: Unsupervised surgical instrument segmentation via anchor generation and semantic diffusion. In Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI). Cham, Switzerland: Springer, pp. 657\u2013667. (2020)","key":"3451_CR52","DOI":"10.1007\/978-3-030-59716-0_63"},{"unstructured":"Cao, H. et al.: Swin-UNet: UNet-like pure transformer for medical image segmentation. (2021). arXiv: 2105.05537.","key":"3451_CR53"},{"key":"3451_CR54","doi-asserted-by":"publisher","first-page":"102310","DOI":"10.1016\/j.media.2021.102310","volume":"76","author":"ZL Ni","year":"2022","unstructured":"Ni, Z.-L., Zhou, X.-H., Wang, G.-A., Yue, W.-Q., Li, Z., Bian, G.-B., Hou, Z.-G.: SurgiNet: pyramid attention aggregation and class-wise self-distillation for surgical instrument segmentation. Med. Image Anal. 76, 102310 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102310","journal-title":"Med. Image Anal."},{"issue":"3","key":"3451_CR55","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1109\/TMRB.2022.3193420","volume":"4","author":"L Yang","year":"2022","unstructured":"Yang, L., Gu, Y., Bian, G., Liu, Y.: DRR-Net: a dense-connected residual recurrent convolutional network for surgical instrument segmentation from endoscopic images. IEEE Trans. Med. Robot. Bionics 4(3), 696\u2013707 (2022)","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"3451_CR56","doi-asserted-by":"publisher","first-page":"102327","DOI":"10.1016\/j.media.2021.102327","volume":"76","author":"H Wu","year":"2022","unstructured":"Wu, H., Chen, S., Chen, G., Wang, W., Lei, B., Wen, Z.: FAT-Net: feature adaptive transformers for automated skin lesion segmentation. Med. Image Anal. 76, 102327 (2022). https:\/\/doi.org\/10.1016\/j.media.2021.102327.","journal-title":"Med. Image Anal."},{"issue":"2","key":"3451_CR57","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1109\/TMRB.2024.3359303","volume":"6","author":"Z Wang","year":"2024","unstructured":"Wang, Z., Li, Z., Yu, X., Jia, Z., Xu, X., Schuller, B.W.: Cross-scene semantic segmentation for medical surgical instruments using structural similarity-based partial activation networks. IEEE Trans. Med. Robot. Bion. 6(2), 399\u2013409 (2024). https:\/\/doi.org\/10.1109\/TMRB.2024.3359303","journal-title":"IEEE Trans. Med. Robot. Bion."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03451-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03451-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03451-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T17:53:22Z","timestamp":1726250002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03451-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,31]]},"references-count":58,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["3451"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03451-3","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"type":"print","value":"1863-1703"},{"type":"electronic","value":"1863-1711"}],"subject":[],"published":{"date-parts":[[2024,8,31]]},"assertion":[{"value":"19 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study, \u201cTransfer Learning for Surgical Instrument Segmentation,\u201d used publicly available images from Endovis 2017 and Neuro Surgical Tools (NST) databases, which did not require additional ethics approval or individual consent due to their intended research purpose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}