{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T04:56:51Z","timestamp":1775624211387,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,5,4]],"date-time":"2024-05-04T00:00:00Z","timestamp":1714780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research, Development, and Innovation Fund of Hungary","award":["TKP2021-NKTA-3"],"award-info":[{"award-number":["TKP2021-NKTA-3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only visual information is available and they cannot use their tactile senses during keyhole surgeries. This is the case with laparoscopic hysterectomy since some organs are also difficult to distinguish based on visual information, making laparoscope-based hysterectomy challenging. In this paper, we propose a solution based on semantic segmentation, which can create pixel-accurate predictions of surgical images and differentiate the uterine arteries, ureters, and nerves. We trained three binary semantic segmentation models based on the U-Net architecture with the EfficientNet-b3 encoder; then, we developed two ensemble techniques that enhanced the segmentation performance. Our pixel-wise ensemble examines the segmentation map of the binary networks on the lowest level of pixels. The other algorithm developed is a region-based ensemble technique that takes this examination to a higher level and makes the ensemble based on every connected component detected by the binary segmentation networks. We also introduced and trained a classic multi-class semantic segmentation model as a reference and compared it to the ensemble-based approaches. We used 586 manually annotated images from 38 surgical videos for this research and published this dataset.<\/jats:p>","DOI":"10.3390\/s24092926","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T14:26:11Z","timestamp":1715005571000},"page":"2926","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6862-757X","authenticated-orcid":false,"given":"Norbert","family":"Serban","sequence":"first","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Kupas","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-9770","authenticated-orcid":false,"given":"Andras","family":"Hajdu","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"T\u00f6r\u00f6k","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynaecology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4405-2040","authenticated-orcid":false,"given":"Balazs","family":"Harangi","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, University of Debrecen, 4028 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aarts, J.W., Nieboer, T.E., Johnson, N., Tavender, E., Garry, R., Mol, B.W.J., and Kluivers, K.B. (2015). Surgical approach to hysterectomy for benign gynaecological disease. Cochrane Database Syst. Rev., 8.","DOI":"10.1002\/14651858.CD003677.pub5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.bpobgyn.2005.09.007","article-title":"Hysterectomy","volume":"20","author":"Clayton","year":"2006","journal-title":"Best Pract. Res. Clin. Obstet. Gynaecol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1089\/gyn.1989.5.213","article-title":"Laparoscopic hysterectomy","volume":"5","author":"Reich","year":"1989","journal-title":"J. Gynecol. Surg."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.ejogrb.2007.06.004","article-title":"Comparison of laparoscopic and abdominal hysterectomy in terms of quality of life: A systematic review","volume":"136","author":"Kluivers","year":"2008","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S1074-3804(05)80831-X","article-title":"Assessments of laparoscopic-assisted vaginal hysterectomy","volume":"2","author":"Councell","year":"1994","journal-title":"J. Am. Assoc. Gynecol. Laparosc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"447","DOI":"10.5694\/j.1326-5377.1993.tb137962.x","article-title":"Laparoscopic hysterectomy: A series of 100 cases","volume":"159","author":"Jones","year":"1993","journal-title":"Med. J. Aust."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1016\/S0002-9378(11)91584-1","article-title":"Ureter injury during laparoscopy-assisted vaginal hysterectomy with the endoscopic linear stapler","volume":"167","author":"Woodland","year":"1992","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_8","first-page":"106","article-title":"Ureteral injury in laparoscopic gynecologic surgery","volume":"5","author":"Manoucheri","year":"2012","journal-title":"Rev. Obstet. Gynecol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1007\/s00464-012-2539-2","article-title":"Causes and prevention of laparoscopic ureter injuries: An analysis of 31 cases during laparoscopic hysterectomy in the Netherlands","volume":"27","author":"Janssen","year":"2013","journal-title":"Surg. Endosc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1097\/00006254-199609000-00023","article-title":"Early complications of laparoscopic hysterectomy","volume":"51","author":"Harris","year":"1996","journal-title":"Obstet. Gynecol. Surv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/S1074-3804(96)80070-3","article-title":"Routine use of ureteric catheters at laparoscopic hysterectomy may cause unnecessary complications","volume":"3","author":"Wood","year":"1996","journal-title":"J. Am. Assoc. Gynecol. Laparosc."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1016\/j.juro.2007.06.049","article-title":"Real-time intraoperative ureteral guidance using invisible near-infrared fluorescence","volume":"178","author":"Tanaka","year":"2007","journal-title":"J. Urol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dougherty, G. (2009). Digital Image Processing for Medical Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511609657"},{"key":"ref_14","unstructured":"Bankman, I. (2008). Handbook of Medical Image Processing and Analysis, Elsevier."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/TMI.2016.2521800","article-title":"A CNN regression approach for real-time 2D\/3D registration","volume":"35","author":"Miao","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Harangi, B., Hajdu, A., Lampe, R., and Torok, P. (2017, January 18\u201320). Differentiating ureter and arteries in the pelvic via endoscope using deep neural network. Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis, Ljubljana, Slovenia.","DOI":"10.1109\/ISPA.2017.8073574"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1109\/ACCESS.2018.2889138","article-title":"Laparoscopic image-guided system based on multispectral imaging for the ureter detection","volume":"7","author":"Yu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Taha, A., Lo, P., Li, J., and Zhao, T. (2018, January 16\u201320). Kid-net: Convolution networks for kidney vessels segmentation from ct-volumes. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00937-3_53"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kletz, S., Schoeffmann, K., Benois-Pineau, J., and Husslein, H. (2019, January 4\u20136). Identifying surgical instruments in laparoscopy using deep learning instance segmentation. Proceedings of the 2019 International Conference on Content-Based Multimedia Indexing (CBMI), Dublin, Ireland.","DOI":"10.1109\/CBMI.2019.8877379"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_22","unstructured":"Hasan, S.K., and Linte, C.A. (2019, January 23\u201327). U-NetPlus: A modified encoder-decoder U-Net architecture for semantic and instance segmentation of surgical instruments from laparoscopic images. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany."},{"key":"ref_23","unstructured":"Maqbool, S., Riaz, A., Sajid, H., and Hasan, O. (2020). m2caiseg: Semantic segmentation of laparoscopic images using convolutional neural networks. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","article-title":"Ensemble learning: A survey","volume":"8","author":"Sagi","year":"2018","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_25","unstructured":"Knerr, S., Personnaz, L., and Dreyfus, G. (1990). Neurocomputing, Springer."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/72.363444","article-title":"Efficient classification for multiclass problems using modular neural networks","volume":"6","author":"Anand","year":"1995","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/j.patcog.2011.01.017","article-title":"An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes","volume":"44","author":"Galar","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s10278-019-00197-0","article-title":"Automated cardiovascular pathology assessment using semantic segmentation and ensemble learning","volume":"33","author":"Lindsey","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khan, I.A., Asaduzzaman, F., and Fattah, S.A. (2020, January 17\u201319). An Automatic Ocular Disease Detection Scheme from Enhanced Fundus Images Based on Ensembling Deep CNN Networks. Proceedings of the 2020 11th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh.","DOI":"10.1109\/ICECE51571.2020.9393050"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1882","DOI":"10.1109\/JSTARS.2019.2915259","article-title":"Deep learning ensemble for hyperspectral image classification","volume":"12","author":"Chen","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alosaimi, N., and Alhichri, H. (2020, January 19\u201321). Fusion of CNN ensemble for Remote Sensing Scene Classification. Proceedings of the 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), Riyadh, Saudi Arabia.","DOI":"10.1109\/ICCAIS48893.2020.9096721"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1109\/TITS.2019.2918923","article-title":"Ensemble convolutional neural networks for mode inference in smartphone travel survey","volume":"21","author":"Yazdizadeh","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","unstructured":"(2024, March 04). UD Ureter-Artery-Nerve Dataset. Norbert Serban. UD Ureter-Uterine Artery-Nerve Dataset. IEEE Dataport. Available online: https:\/\/dx.doi.org\/10.21227\/q2dd-yt09."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e2325572","DOI":"10.1080\/17452759.2024.2325572","article-title":"AM-SegNet for additive manufacturing in situ X-ray image segmentation and feature quantification","volume":"19","author":"Li","year":"2024","journal-title":"Virtual Phys. Prototyp."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2329675","DOI":"10.1080\/24699322.2024.2329675","article-title":"SwinD-Net: A lightweight segmentation network for laparoscopic liver segmentation","volume":"29","author":"Ouyang","year":"2024","journal-title":"Comput. Assist. Surg."},{"key":"ref_37","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_38","first-page":"241","article-title":"Distribution de la flore alpine dans le bassin des Dranses et dans quelques r\u00e9gions voisines","volume":"37","author":"Jaccard","year":"1901","journal-title":"Bull Soc. Vaudoise Sci. Nat."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2926\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:39:27Z","timestamp":1760107167000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/9\/2926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,4]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24092926"],"URL":"https:\/\/doi.org\/10.3390\/s24092926","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,4]]}}}