Generalizable fetal brain biometry via adaptive artificial intelligence

Yijin Li; Mingxuan Liu; Yi Liao; Le Chang; Hongjia Yang; Kasidit Anmahapong; Xiaotian Hu; Ziang Wang; Yingqi Hao; Haoxiang Li; Yifei Chen; Zihan Li; Ziyu Li; Juncheng Zhu; Min Kang; Yan Song; Hua Lai; Xiaoling Zhou; Dingzhi Yue; Xu Liu; Nan Sun; Rong Hu; Xuesheng Li; Fenglin Jia; Gang Ning; Haibo Qu; Qiyuan Tian

arXiv · 2610.23966

Generalizable fetal brain biometry via adaptive artificial intelligence

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Abstract

Reliable fetal brain biometry on magnetic resonance imaging (MRI) is essential for diagnosing congenital brain anomalies, but artificial intelligence models often lose accuracy under domain shifts. We developed BioTTA, a backbone-agnostic, source-free unsupervised test-time adaptation framework that adapts pretrained models online to individual unlabeled target volumes. This retrospective multicohort study included 394 reconstructed 3D fetal brain MRI volumes at 20-39 gestational weeks, with 11 biometric measurements annotated by radiologists. BioTTA was developed on single-center turbo spin-echo MRI and evaluated on three datasets representing cross-sequence, cross-center, and cross-nation shifts. Biometry accuracy was assessed using mean absolute error (MAE) and the percentage of correct measurements within 4 mm. A web-based reporting platform mapped measurements onto normative growth trajectories and was evaluated against radiologists in four multicenter disease cohorts covering germinal matrix-intraventricular hemorrhage, ventriculomegaly, and cerebellar abnormality. Across all three target datasets, BioTTA outperformed auto-SVRTK, source-only models, and FetalSynthGen, while matching models finetuned with target labels. MAEs ranged from 1.10 to 1.46 mm with a convolutional backbone and from 1.46 to 2.25 mm with a transformer backbone. Diagnostic accuracy ranged from 82.1% to 95.0%, comparable to a senior radiologist. BioTTA supports accurate, generalizable fetal brain biometry and standardized prenatal brain growth assessment across heterogeneous MRI sources.

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Yijin Li, Mingxuan Liu, Yi Liao, Le Chang, Hongjia Yang, Kasidit Anmahapong, Xiaotian Hu, Ziang Wang, Yingqi Hao, Haoxiang Li, Yifei Chen, Zihan Li, Ziyu Li, Juncheng Zhu, Min Kang, Yan Song, Hua Lai, Xiaoling Zhou, Dingzhi Yue, Xu Liu, Nan Sun, Rong Hu, Xuesheng Li, Fenglin Jia, Gang Ning, Haibo Qu, Qiyuan Tian. 2026-10-11. Generalizable fetal brain biometry via adaptive artificial intelligence. https://arxiv.org/abs/2610.23966

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