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Increased NT is strongly associated with chromosomal abnormalities, structural defects, and other adverse outcomes. However, the low signal\u2010to\u2010noise ratio (SNR), numerous artifacts, and poor contrast present in ultrasound images pose considerable challenges to existing segmentation methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address these challenges, this paper proposes a novel Transformer model, TransNTSeg, for NT segmentation. This study serves as a preliminary proof\u2010of\u2010concept to evaluate the feasibility of the proposed architecture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A clinical dataset of 123 B\u2010mode ultrasound images is collected. To ensure rigorous evaluation, the dataset is partitioned into 98 images for training\/validation (using 5\u2010fold cross\u2010validation) and 25 images for independent testing. The architecture incorporates two main innovations: first, an enhanced Dense Dilated Mixed Feed\u2010Forward Network (DDMix\u2010FFN) module within the Transformer that more robustly integrates global dependencies and local context to handle image noise and boundary ambiguity; and second, a DDMix\u2010FFN Transformer context bridge that fuses multi\u2010scale encoder features for precise localization of the small NT region. Performance is compared against eight state\u2010of\u2010the\u2010art benchmarks (e.g., U\u2010Net, MT\u2010UNet) using Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). Statistical significance is assessed using the paired\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test with Bonferroni correction to control for multiple comparisons (adjusted\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.0015625), and practical significance is quantified using Cohen's\n                      <jats:italic>\n                        d\n                        <jats:sub>z<\/jats:sub>\n                      <\/jats:italic>\n                      effect size.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      TransNTSeg achieves the highest performance on the test dataset, with a mean IoU of 0.763\u00a0\u00b1\u00a00.05 and a mean DSC of 0.852\u00a0\u00b1\u00a00.03. Rigorous statistical analysis (Bonferroni corrected\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.0015625) confirms considerable improvements. Based on the evaluation of effect sizes, TransNTSeg is identified as the best\u2010performing method, demonstrating a substantial effect size (\n                      <jats:italic>d<\/jats:italic>\n                      \u00a0=\u00a00.745) against the closest competitor (MT\u2010UNet).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>While demonstrating competitive performance within this controlled setting, these promising outcomes highlight the potential of TransNTSeg for accurate NT segmentation. This warrants further validation on multicenter, multi\u2010vendor datasets to establish its generalizability for broad clinical use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70418","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:36:15Z","timestamp":1776080175000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A preliminary study of a hybrid transformer with enhanced local feature fusion for nuchal translucency ultrasound image segmentation"],"prefix":"10.1002","volume":"53","author":[{"given":"Shuang","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology Peking University First Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingling","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology Beijing University of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huixia","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology Peking University First Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Artificial Intelligence Beijing Wuzi University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology Beijing University of Technology  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.ruq.0000263846.00185.61"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118477076.ch9"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2004.03.090"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajog.2014.06.025"},{"issue":"5","key":"e_1_2_9_6_1","first-page":"936","article-title":"The associations of nuchal translucency and fetal abnormalities; 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