{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:07:38Z","timestamp":1789391258731,"version":"build-2803163510"},"reference-count":43,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"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. Clin. Med.","JCM"],"abstract":"<jats:p>Background\/Objectives: Accurate fetal head biometry, obtained via ultrasound imaging, is fundamental to prenatal assessment, as it enables fetal growth to be monitored and developmental abnormalities to be detected early. However, conventional manual measurements are time-consuming and operator-dependent, as well as being prone to inter-observer variability. This retrospective study proposes investigating whether conventional segmentation metrics accurately reflect downstream fetal biometric measurements. This would be achieved by evaluating three deep learning architectures on expert-selected, standard-plane fetal head ultrasound images. Methods: Three deep learning architectures, namely, U-Net, Residual U-Net and TransUNet were implemented and evaluated under identical conditions using a local clinical ultrasound dataset of 1918 images and masks from 206 pregnancies. Following segmentation, ellipse fitting was applied to both predicted and reference masks to derive head circumference (HC), biparietal diameter (BPD), and occipitofrontal diameter (OFD). Image-specific physical scaling was applied to convert pixel-based measurements into millimeters. The performance of models was assessed using conventional segmentation metrics, including the Dice Similarity Coefficient (DSC), the Intersection over Union (IoU), and the Hausdorff Distance (HD) metrics. This was complemented by a clinically meaningful biometric evaluation based on the mean absolute error (MAE) of the HC, BPD, and OFD measurements. Results: All three architectures achieved high segmentation performance, with DSC values above 0.98 and IoU values above 0.96. Residual U-Net produced the highest DSC and IoU values (0.9819 \u00b1 0.0102 and 0.9647 \u00b1 0.0196, respectively), whereas TransUNet produced the lowest HD value (1.8935 \u00b1 0.8570 mm). For biometric measurements, the Residual U-Net produced the lowest MAE for HC (1.8877 \u00b1 1.7689 mm) and OFD (0.8672 \u00b1 0.8342 mm), while the TransUNet resulted in the lowest BPD MAE (0.7504 \u00b1 0.7045 mm). Pairwise statistical analysis revealed significant differences in DSC between U-Net and Residual U-Net (p = 0.0172), as well as between Residual U-Net and TransUNet (p = 0.0119). However, no significant differences were observed between U-Net and TransUNet (p = 0.4726). For the biometric measurements, significant differences were only observed for the BPD MAE between the U-Net and TransUNet models (p = 0.0385), while the HC and OFD errors were statistically comparable across the model pairs. Conclusions: This study shows that high overlap-based segmentation performance does not necessarily lead to statistically significant or superior biometric measurement accuracy. The results emphasize the importance of using both conventional segmentation metrics and downstream, measurement-oriented evaluations when comparing deep learning models for fetal head biometry. However, as this study involved a retrospective technical evaluation of curated, expert-selected standard-plane images, the findings could not be interpreted as evidence of clinical effectiveness or immediate clinical applicability. Further validation is required using pregnancy-level separation, multiple observers, independent clinical measurements, pathological and technically challenging examinations, and multicenter external datasets.<\/jats:p>","DOI":"10.3390\/jcm15187127","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:09:51Z","timestamp":1789387791000},"page":"7127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Measurement-Oriented Evaluation of Deep Learning Models for Automated Fetal Head Biometry in Prenatal Ultrasound"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2021-6311","authenticated-orcid":false,"given":"Hilal G\u00fcls\u00fcm Turan","family":"\u00d6zsoy","sequence":"first","affiliation":[{"name":"Department of Radiology, Bursa City Hospital, 16250 Bursa, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Ebrar Elest","family":"\u015een","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Bilecik \u015eeyh Edebali University, 11100 Bilecik, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Furkan Ert\u00fcrk","family":"Urfal\u0131","sequence":"additional","affiliation":[{"name":"Department of Radiology, Bursa City Hospital, 16250 Bursa, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"given":"G\u00fcltekin Adana\u015f","family":"Ayd\u0131n","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Bursa City Hospital, 16250 Bursa, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Behi\u00e7","family":"Aky\u00fcz","sequence":"additional","affiliation":[{"name":"Department of Radiology, Bursa City Hospital, 16250 Bursa, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"validation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6559-1399","authenticated-orcid":false,"given":"Emre","family":"Dand\u0131l","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, Bilecik \u015eeyh Edebali University, 11100 Bilecik, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105771","DOI":"10.1016\/j.cmpb.2020.105771","article-title":"A regression framework to head-circumference delineation from US fetal images","volume":"198","author":"Fiorentino","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Paladini, D., and Volpe, P. (2024). Ultrasound of Congenital Fetal Anomalies: Differential Diagnosis and Prognostic Indicators, CRC Press.","DOI":"10.1201\/9781003048268"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1002\/uog.20272","article-title":"ISUOG Practice Guidelines: Ultrasound assessment of fetal biometry and growth","volume":"53","author":"Salomon","year":"2019","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/s10278-020-00410-5","article-title":"Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-Net","volume":"34","author":"Zeng","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1002\/uog.24888","article-title":"ISUOG Practice Guidelines (updated): Performance of the routine mid-trimester fetal ultrasound scan","volume":"59","author":"Salomon","year":"2022","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103614","DOI":"10.1016\/j.media.2025.103614","article-title":"ScanAhead: Simplifying standard plane acquisition of fetal head ultrasound","volume":"104","author":"Men","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1002\/uog.12342","article-title":"ISUOG practice guidelines: Performance of first-trimester fetal ultrasound scan","volume":"41","author":"Salomon","year":"2013","journal-title":"Ultrasound Obstet. Gynecol. Off. J. Int. Soc. Ultrasound Obstet. Gynecol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1002\/uog.15956","article-title":"Scientific basis for standardization of fetal head measurements by ultrasound: A reproducibility study","volume":"48","author":"Napolitano","year":"2016","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sobhaninia, Z., Rafiei, S., Emami, A., Karimi, N., Najarian, K., Samavi, S., and Soroushmehr, S.R. (2019). Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC.2019.8856981"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1148\/radiology.152.2.6739822","article-title":"Estimating fetal age: Computer-assisted analysis of multiple fetal growth parameters","volume":"152","author":"Hadlock","year":"1984","journal-title":"Radiology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1259\/bjr.75.891.750215","article-title":"Assessment of fetal anatomy in the first trimester using two-and three-dimensional ultrasound","volume":"75","author":"Michailidis","year":"2002","journal-title":"Br. J. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TMI.2017.2712367","article-title":"SonoNet: Real-time detection and localisation of fetal standard scan planes in freehand ultrasound","volume":"36","author":"Baumgartner","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1002\/uog.10082","article-title":"Intra-and interobserver variability in fetal ultrasound measurements","volume":"39","author":"Sarris","year":"2012","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"van den Heuvel, T.L., de Bruijn, D., de Korte, C.L., and Ginneken, B.V. (2018). Automated measurement of fetal head circumference using 2D ultrasound images. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0200412"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Budd, S., Sinclair, M., Khanal, B., Matthew, J., Lloyd, D., Gomez, A., Toussaint, N., Robinson, E.C., and Kainz, B. (2019). Confident head circumference measurement from ultrasound with real-time feedback for sonographers. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-32251-9_75"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"065009","DOI":"10.1088\/1361-6579\/ab21ac","article-title":"Automatic evaluation of fetal head biometry from ultrasound images using machine learning","volume":"40","author":"Kim","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1007\/s13224-021-01574-y","article-title":"Sonographic estimation of the fetal head circumference: Accuracy and factors affecting the error","volume":"72","author":"Poojari","year":"2022","journal-title":"J. Obstet. Gynecol. India"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7047","DOI":"10.1038\/s41467-023-42438-5","article-title":"Fetal biometry and amniotic fluid volume assessment end-to-end automation using Deep Learning","volume":"14","author":"Slimani","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_19","first-page":"e100534","article-title":"A Modular Deep-Learning Framework for Automated Fetal Biometry: Proof-of-Concept Evaluation of Head, Abdominal, and Femur Measurements","volume":"17","author":"Sundaramoorthy","year":"2025","journal-title":"Cureus"},{"key":"ref_20","first-page":"36","article-title":"Image processing with ImageJ","volume":"11","author":"Ram","year":"2004","journal-title":"Biophotonics Int."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"NnU-Net: A self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), IEEE.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_24","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., and Li, Y. (2022). Maxvit: Multi-axis vision transformer. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-031-20053-3_27"},{"key":"ref_26","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ba\u015ftu\u011f, B.T., G\u00fcneri, G., Y\u0131ld\u0131r\u0131m, M.S., \u00c7orbac\u0131, K., and Dand\u0131l, E. (2024). Fully automated detection of the appendix using U-net deep learning architecture in CT scans. J. Clin. Med., 13.","DOI":"10.3390\/jcm13195893"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111056","DOI":"10.1016\/j.compbiomed.2025.111056","article-title":"The impact of U-Net architecture choices and skip connections on the robustness of segmentation across texture variations","volume":"197","author":"Kamath","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yang, P., and Zhang, B. (2020). Automatic segmentation in fetal ultrasound images based on improved U-net. Proceedings of the Journal of Physics: Conference Series, IOP.","DOI":"10.1088\/1742-6596\/1693\/1\/012183"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"526","DOI":"10.4218\/etrij.2023-0057","article-title":"Precise segmentation of fetal head in ultrasound images using improved U-Net model","volume":"46","author":"Nagabotu","year":"2024","journal-title":"ETRI J."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105693","DOI":"10.1016\/j.compbiomed.2022.105693","article-title":"FCRB U-Net: A novel fully connected residual block U-Net for fetal cerebellum ultrasound image segmentation","volume":"148","author":"Shu","year":"2022","journal-title":"Comput. Biol. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bhilwarawala, A., and Bandyopadhyay, M. (2025). Attention-ResUNet for Automated Fetal Head Segmentation. Proceedings of the International Conference on Advanced Network Technologies and Intelligent Computing, Springer.","DOI":"10.1007\/978-3-032-27117-4_30"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Isensee, F., and Maier-Hein, K.H. (2019). An attempt at beating the 3D U-Net. arXiv.","DOI":"10.24926\/548719.001"},{"key":"ref_37","unstructured":"Zimerman, I., and Wolf, L. (2023). On the long range abilities of transformers. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103280","DOI":"10.1016\/j.media.2024.103280","article-title":"TransUNet: Rethinking the U-Net architecture design for medical image segmentation through the lens of transformers","volume":"97","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1177\/01617346211069882","article-title":"Fast and accurate U-net model for fetal ultrasound image segmentation","volume":"44","author":"Shabanzadeh","year":"2022","journal-title":"Ultrason. Imaging"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10278-023-00908-8","article-title":"Fetal ultrasound segmentation and measurements using appearance and shape prior based density regression with deep CNN and robust ellipse fitting","volume":"37","author":"Dubey","year":"2024","journal-title":"J. Imaging Inform. Med."},{"key":"ref_41","unstructured":"Kawadkar, K. (2025). Comparative analysis of vision transformers and convolutional neural networks for medical image classification. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109507","DOI":"10.1016\/j.compbiomed.2024.109507","article-title":"A review of convolutional neural network based methods for medical image classification","volume":"185","author":"Chen","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/JBHI.2017.2703890","article-title":"Automatic fetal head circumference measurement in ultrasound using random forest and fast ellipse fitting","volume":"22","author":"Li","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/18\/7127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:53:10Z","timestamp":1789390390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/15\/18\/7127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":43,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jcm15187127"],"URL":"https:\/\/doi.org\/10.3390\/jcm15187127","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]}}}