{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T17:53:12Z","timestamp":1787161992855,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of Fujian","award":["2023J011784"],"award-info":[{"award-number":["2023J011784"]}]},{"name":"National Natural Science Foundation of Fujian","award":["2025J011577"],"award-info":[{"award-number":["2025J011577"]}]},{"name":"Quanzhou scientific and technological planning projects","award":["2023NS055"],"award-info":[{"award-number":["2023NS055"]}]},{"name":"Quanzhou scientific and technological planning projects","award":["2025QZNY075"],"award-info":[{"award-number":["2025QZNY075"]}]},{"name":"Joint Funds for the innovation of science and Technology, Fujian province","award":["2024Y9386"],"award-info":[{"award-number":["2024Y9386"]}]},{"name":"Joint Funds for the innovation of science and Technology, Fujian province","award":["2025Y9505"],"award-info":[{"award-number":["2025Y9505"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>This study addresses the challenge of the difficult identification of organ structures in the standard sections of fetal abdominal ultrasounds. A deep learning-based multi-task model named SEG-FAUSP was developed to segment the core anatomical structures of seven key fetal abdominal ultrasound sections. We collected fetal abdominal ultrasound images from pregnant women in the mid-pregnancy period (18\u201324 weeks) using various mainstream ultrasound devices, and professional physicians annotated key anatomical structures (e.g., umbilical veins, gastric bubbles, spine) in the images. Based on an improved deep learning framework, the model accurately segments and locates the target organ structures through a parallel dual-branch semantic segmentation network, which avoids the over-reliance on large-scale pre-trained data in traditional methods. Experimental results show that the model achieves excellent performance in anatomical structure segmentation, with the intersection over union of the bladder and gastric bubble both reaching above 0.84; its segmentation accuracy for complex structures such as the inferior vena cava is also significantly superior to the baseline model. As an end-to-end model, it simplifies the clinical interpretation process of fetal abdominal ultrasound, reduces the risk of missed diagnoses caused by unclear organ identification, provides an efficient auxiliary tool for prenatal screening in grassroots medical institutions, and is of great significance for improving the quality of newborns.<\/jats:p>","DOI":"10.3390\/bioengineering13040403","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:09:18Z","timestamp":1774944558000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SEG-FAUSP: Anatomical Structure Segmentation of the Standard Sections of Fetal Abdominal Ultrasounds"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2451-3103","authenticated-orcid":false,"given":"Jianhui","family":"Chen","sequence":"first","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0809-6364","authenticated-orcid":false,"given":"Peizhong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Engineering, Huaqiao University, Quanzhou 362021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaying","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoling","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiuming","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1578-7076","authenticated-orcid":false,"given":"Zhonghua","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunlan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1186\/s13052-019-0635-9","article-title":"Prevalence, types, and malformations in congenital anomalies of the kidney and urinary tract in newborns: A retrospective hospital-based study","volume":"45","author":"Li","year":"2019","journal-title":"Ital. J. Pediatr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.ejogrb.2023.08.049","article-title":"Accuracy of estimated fetal weight assessment in fetuses with congenital diaphragmatic hernia\u2014Is the Hadlock formula a reliable tool?","volume":"293","author":"Kuchnowska","year":"2024","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Chen, X., Liu, H., Li, Y., Zhang, W., Zhou, A., Xia, W., and Xu, S. (2024). First-trimester fetal size, accelerated growth in utero, and child neurodevelopment in a cohort study. BMC Med., 22.","DOI":"10.1186\/s12916-024-03390-3"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"54310","DOI":"10.1109\/ACCESS.2021.3071301","article-title":"Deep learning in medical ultrasound image analysis: A review","volume":"9","author":"Wang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xiao, X., Zhang, J., Shao, Y., Liu, J., Shi, K., He, C., and Kong, D. (2025). Deep learning-based medical ultrasound image and video segmentation methods: Overview, frontiers, and challenges. Sensors, 25.","DOI":"10.3390\/s25082361"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhou, X., Wan, J., Sun, F., Wang, R., Yan, Y., Li, P., and Wang, C. (2026). A multi-task deep learning and radiomics framework for fetal anatomical structure detection and classification in ultrasound imaging. Sci. Rep.","DOI":"10.1038\/s41598-026-41635-8"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, D., Guo, H., Chen, M., Chen, H., Chen, J., Liang, B., Liang, P., and Tan, Y. (2025). DCD: A semantic segmentation model for fetal ultrasound four-chamber view. BlockChain and Trustworthy Systems, Springer.","DOI":"10.1007\/978-981-95-3480-7_31"},{"key":"ref_8","unstructured":"Wang, F., Silvestre, G., and Curran, K.M. (2026). Dual agreement consistency learning with foundation models for semi-supervised fetal heart ultrasound segmentation and diagnosis. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bai, J., Tang, Y., Zhou, Z., Islam, M., Tabassum, M., Almar-Munoz, E., Liu, H., Meng, H., Lv, N., and Deng, B. (2026). FUGC: Benchmarking semi-supervised learning methods for cervical segmentation. arXiv.","DOI":"10.1109\/TMI.2026.3666364"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.future.2022.04.011","article-title":"An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph","volume":"135","author":"Zhao","year":"2022","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e24427","DOI":"10.1097\/MD.0000000000024427","article-title":"Automatic quality assessment for 2D fetal sonographic standard plane based on multitask learning","volume":"100","author":"Zhang","year":"2021","journal-title":"Medicine"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1002\/uog.21967","article-title":"Using deep-learning algorithms to classify fetal brain ultrasound images as normal or abnormal","volume":"56","author":"Xie","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Guo, M., and Du, Y. (2019). Classification of thyroid ultrasound standard plane images using ResNet-18 networks. International Conference on Anti-Counterfeiting, Security, and Identification, IEEE.","DOI":"10.1109\/ICASID.2019.8925267"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1109\/TCYB.2017.2685080","article-title":"Ultrasound standard plane detection using a composite neural network framework","volume":"47","author":"Chen","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"83821","DOI":"10.1109\/ACCESS.2020.2991845","article-title":"Standard plane identification in fetal brain ultrasound scans using a differential convolutional neural network","volume":"8","author":"Qu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wu, B.Z., Wu, H.L., Du, Y., and Liu, P.Z. (2021). Automatic recognition of fetal heart standard section based on Fast-RCNN. 2021 IEEE 15th International Conference on Anti-Counterfeiting, Security, and Identification (ASID), IEEE.","DOI":"10.1109\/ASID52932.2021.9651487"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7771","DOI":"10.1109\/TII.2021.3069470","article-title":"Automatic fetal ultrasound standard plane recognition based on deep learning and IIoT","volume":"17","author":"Pu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_18","unstructured":"Cai, Y., Sharma, H., Chatelain, P., and Noble, J.A. (2021). Multi-task SonoEyeNet: Detection of fetal standardized planes assisted by generated sonographer attention maps. Medical Image Computing and Computer-Assisted Intervention; Lecture Notes in Computer Science, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liang, J., Huang, R., Kong, P., Li, S., Wang, T., and Lei, B. (2019). SPRNet: Automatic fetal standard plane recognition network for ultrasound images. Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-32875-7_5"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kong, P., Ni, D., Chen, S., Li, S., Wang, T., and Lei, B. (2019). Automatic and efficient standard plane recognition in fetal ultrasound images via multi-scale dense networks. Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-030-00807-9_16"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1002\/jum.16209","article-title":"Early pregnancy fetal facial ultrasound standard plane-assisted recognition algorithm","volume":"42","author":"Xue","year":"2023","journal-title":"J. Ultrasound Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, T., Tsui, P.-H., Leonov, D., Wu, S., Bin, G., and Zhou, Z. (2024). LPC-SonoNet: A lightweight network based on SonoNet and light pyramid convolution for fetal ultrasound standard plane detection. Sensors, 24.","DOI":"10.3390\/s24237510"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1007\/s11517-024-03111-1","article-title":"PSFHSP-Net: An efficient lightweight network for identifying pubic symphysis-fetal head standard plane from intrapartum ultrasound images","volume":"62","author":"Qiu","year":"2024","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_24","first-page":"2573","article-title":"Evaluation of convolutional neural network architecture for feasibility analysis on fetal abdomen and brain images","volume":"11","author":"Deepika","year":"2021","journal-title":"J. Med. Imaging Health Inform."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1016\/j.ultrasmedbio.2018.11.016","article-title":"Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks","volume":"45","author":"Sridar","year":"2019","journal-title":"Ultrasound Med. Biol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ejmp.2021.06.020","article-title":"Automatic fetal biometry prediction using a novel deep convolutional network architecture","volume":"88","author":"Oghli","year":"2021","journal-title":"Phys. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.eswa.2023.122153","article-title":"Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models","volume":"238","author":"Krishna","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2150054","DOI":"10.1142\/S0219691321500545","article-title":"An Improvement for the Automatic Classification Method for Ultrasound Images Used on CNN","volume":"20","author":"Avazov","year":"2022","journal-title":"Int. J. Wavelets Multiresolut. Inf. Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lyu, C., Han, K., Liu, L., Chen, J., Ma, L., Pang, Z., and Liu, Z. (2025). Bidirectional Prototype-Guided Consistency Constraint for Semi-Supervised Fetal Ultrasound Image Segmentation. IEEE J. Biomed. Health Inform.","DOI":"10.1109\/JBHI.2025.3584236"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"119117","DOI":"10.1016\/j.neuroimage.2022.119117","article-title":"Subcortical Segmentation of the Fetal Brain in 3D Ultrasound Using Deep Learning","volume":"254","author":"Hesse","year":"2022","journal-title":"NeuroImage"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100075","DOI":"10.1016\/j.wfumbo.2024.100075","article-title":"Detection of Fetal Congenital Heart Defects on Three-Vessel View Ultrasound Videos","volume":"2","author":"Patey","year":"2024","journal-title":"WFUMB Ultrasound Open"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/978-981-97-1335-6_5","article-title":"Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans","volume":"1166","author":"Mohammad","year":"2024","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_33","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_34","unstructured":"Guo, M., Lu, C., Hou, Q., Liu, Z., Cheng, M., and Hu, S. (2022). SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation. Advances in Neural Information Processing Systems 35, Curran Associates Inc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, X., Ke, Z., Zhang, W., and Lau, R. (2023). BiFormer: Vision Transformer with Bi-Level Routing Attention. Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52729.2023.00995"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (2019). CCNet: Criss-Cross Attention for Semantic Segmentation. IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2018.05.011","article-title":"Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function","volume":"309","author":"Hu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017). Focal loss for dense object detection. IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_39","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Taha, A.A., and Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med. Imaging, 15.","DOI":"10.1186\/s12880-015-0068-x"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rahman, M.A., and Wang, Y. (2016). Optimizing intersection-over-union in deep neural networks for image segmentation. Advances in Visual Computing, Springer. International Symposium on Visual Computing.","DOI":"10.1007\/978-3-319-50835-1_22"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1016\/0262-8856(92)90076-F","article-title":"Surface shape and curvature scales","volume":"10","author":"Koenderink","year":"1992","journal-title":"Image Vis. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Computer Vision\u2014ECCV 2018, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_44","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2021). Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation. arXiv."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/4\/403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:35:51Z","timestamp":1774946151000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/4\/403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["bioengineering13040403"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13040403","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]}}}