{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T15:53:07Z","timestamp":1787673187199,"version":"build-2736575974"},"reference-count":24,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T00:00:00Z","timestamp":1697414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Computer-assisted technologies have made significant progress in fetoscopic laser surgery, including placental vessel segmentation. However, the intra- and inter-procedure variabilities in the state-of-the-art segmentation methods remain a significant hurdle. To address this, we investigated the use of conditional generative adversarial networks (cGANs) for fetoscopic image segmentation and compared their performance with the benchmark U-Net technique for placental vessel segmentation. Two deep-learning models, U-Net and pix2pix (a popular cGAN model), were trained and evaluated using a publicly available dataset and an internal validation set. The overall results showed that the pix2pix model outperformed the U-Net model, with a Dice score of 0.80 [0.70; 0.86] versus 0.75 [0.0.60; 0.84] (p-value &lt; 0.01) and an Intersection over Union (IoU) score of 0.70 [0.61; 0.77] compared to 0.66 [0.53; 0.75] (p-value &lt; 0.01), respectively. The internal validation dataset further validated the superiority of the pix2pix model, achieving Dice and IoU scores of 0.68 [0.53; 0.79] and 0.59 [0.49; 0.69] (p-value &lt; 0.01), respectively, while the U-Net model obtained scores of 0.53 [0.49; 0.64] and 0.49 [0.17; 0.56], respectively. This study successfully compared U-Net and pix2pix models for placental vessel segmentation in fetoscopic images, demonstrating improved results with the cGAN-based approach. However, the challenge of achieving generalizability still needs to be addressed.<\/jats:p>","DOI":"10.3390\/jimaging9100226","type":"journal-article","created":{"date-parts":[[2023,10,16]],"date-time":"2023-10-16T08:32:57Z","timestamp":1697445177000},"page":"226","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Placental Vessel Segmentation Using Pix2pix Compared to U-Net"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-6799","authenticated-orcid":false,"given":"Anouk","family":"van der Schot","sequence":"first","affiliation":[{"name":"Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esther","family":"Sikkel","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8170-1249","authenticated-orcid":false,"given":"Mar\u00e8ll","family":"Niekolaas","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Spaanderman","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands"},{"name":"Obstetrics & Gynecology, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands"},{"name":"Department of GROW, School for Oncology and Reproduction, Maastricht University, 6229 ER Maastricht, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8014-0622","authenticated-orcid":false,"given":"Guido","family":"de Jong","sequence":"additional","affiliation":[{"name":"3D Lab, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"key":"ref_1","first-page":"107","article-title":"Twin-to-twin transfusion syndrome (TTTS)","volume":"39","author":"Baschat","year":"2011","journal-title":"J. Perinat. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1002\/pd.6225","article-title":"Computer-assisted fetal laser surgery in the treatment of twin-to-twin transfusion syndrome: Recent trends and prospects","volume":"42","author":"Sikkel","year":"2022","journal-title":"Prenat. Diagn."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bano, S., Vasconcelos, F., Shepherd, L.M., Poorten, E.V., Vercauteren, T., Ourselin, S., David, A.L., Deprest, J., and Stoyanov, D. (2020, January 4\u20138). Deep placental vessel segmentation for fetoscopic mosaicking. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59716-0_73"},{"key":"ref_4","unstructured":"Bano, S., Casella, A., Vasconcelos, F., Qayyum, A., Benzinou, A., Mazher, M., Meriaudeau, F., Lena, C., Cintorrino, I.A., and De Paolis, G.R. (2022). FetReg2021: A Challenge on Placental Vessel Segmentation and Registration in Fetoscopy. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Patnaik, P., Khodee, A., Vasam, G., Mukherjee, A., Salsabili, S., Ukwatta, E., Grynspan, D., Chan, A.D., and Bainbridge, S. (2023). Automated Detection of Maternal Vascular Malperfusion Lesions of the Placenta using Machine Learning. medRxiv, 2023.2006.","DOI":"10.1101\/2023.06.26.23291920"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Rabbani, A., and Babaei, M. (2022). Resolution enhancement of placenta histological images using deep learning. arXiv.","DOI":"10.11159\/icsta22.155"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103448","DOI":"10.1016\/j.micron.2023.103448","article-title":"Automated segmentation and morphological characterization of placental intervillous space based on a single labeled image","volume":"169","author":"Rabbani","year":"2023","journal-title":"Micron"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Salsabili, S., Mukherjee, A., Ukwatta, E., Chan, A.D., Bainbridge, S., and Grynspan, D. (2019). Automated segmentation of villi in histopathology images of placenta. Comput. Biol. Med., 113.","DOI":"10.1016\/j.compbiomed.2019.103420"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s11548-018-1886-4","article-title":"Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery","volume":"14","author":"Sadda","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Casella, A., Moccia, S., Cintorrino, I.A., De Paolis, G.R., Bicelli, A., Paladini, D., De Momi, E., and Mattos, L.S. (2022, January 23\u201327). Deep-Learning Architectures for Placenta Vessel Segmentation in TTTS Fetoscopic Images. Proceedings of the International Conference on Image Analysis and Processing, Lecce, Italy.","DOI":"10.1007\/978-3-031-13324-4_13"},{"key":"ref_11","unstructured":"Bano, S., Casella, A., Vasconcelos, F., Moccia, S., Attilakos, G., Wimalasundera, R., David, A.L., Paladini, D., Deprest, J., and De Momi, E. (2021). FetReg: Placental vessel segmentation and registration in fetoscopy challenge dataset. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xun, S., Li, D., Zhu, H., Chen, M., Wang, J., Li, J., Chen, M., Wu, B., Zhang, H., and Chai, X. (2022). Generative adversarial networks in medical image segmentation: A review. Comput. Biol. Med., 140.","DOI":"10.1016\/j.compbiomed.2021.105063"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Popescu, D., Deaconu, M., Ichim, L., and Stamatescu, G. (2021, January 22\u201325). Retinal blood vessel segmentation using pix2pix gan. Proceedings of the 29th Mediterranean Conference on Control and Automation (MED), Puglia, Italy.","DOI":"10.1109\/MED51440.2021.9480169"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s10278-018-0126-3","article-title":"Towards accurate segmentation of retinal vessels and the optic disc in fundoscopic images with generative adversarial networks","volume":"32","author":"Son","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","article-title":"The distribution of the flora in the alpine zone. 1","volume":"11","author":"Jaccard","year":"1912","journal-title":"New Phytol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in vision: A survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tsuda, H., and Hotta, K. (2019, January 16\u201317). Cell image segmentation by integrating pix2pixs for each class. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00139"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","article-title":"U-net and its variants for medical image segmentation: A review of theory and applications","volume":"9","author":"Siddique","year":"2021","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, Y., Huang, J., and Xie, Y. (2019, January 16\u201317). Enhanced pix2pix dehazing network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00835"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1109\/TIP.2022.3228497","article-title":"UIU-Net: U-Net in U-Net for infrared small object detection","volume":"32","author":"Wu","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102008","DOI":"10.1016\/j.media.2021.102008","article-title":"A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation","volume":"70","author":"Casella","year":"2021","journal-title":"Med. 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