Ultra-ECP: Ellipse-Constrained and Point-Robust Foundation Model Adaptation for Fetal Cardiac Ultrasound Segmentation

Minh H. N. Le, Khanh T. Q. Le, Tuan Vinh, Thanh-Huy Nguyen, Han H. Huynh, Khoa D. Pham, Anh Mai Vu, Hien Quang Kha, Phat Ky Nguyen, Ulas Bagci, Min Xu, Carl Yang, Phat Kim Huynh, Nguyen Quoc Khanh Le

Keywords: Fetal cardiac ultrasound, cardiac segmentation, foundation models, parameter- efficient learning, prenatal biometrics

Abstract: Accurate fetal cardiac segmentation from four-chamber ultrasound images is essential for reliable prenatal biometrics, yet general-purpose foundation models such as SAM remain sensitive to point-prompt placement, produce anatomically inconsistent masks, and require costly full-model fine-tuning. We introduce Ultra-ECP, a parameter-efficient framework that adapts UltraSAM for robust single-point fetal cardiac segmentation. Ultra-ECP integrates three components: (i) a LoRA-based adaptation applied to the prompt encoder and mask decoder, reducing trainable parameters by over 98%; (ii) an Ellipse-Aware Loss that regularizes predictions toward anatomically plausible elliptical cardiac shapes; and (iii) a Point-Robust Augmentation strategy that simulates click imprecision to enhance robustness. Evaluated on the FOCUS dataset, Ultra-ECP outperforms SAM, MedSAM, and fine-tuned UNet baselines. For thoracic segmentation, the method achieves a mean DSC of 95.09% and HD95 of 25.96 px. For cardiac segmentation, the primary target, Ultra-ECP obtains a mean DSC of 92.60% and HD95 of 18.25 px, while maintaining stability under point displacements up to 10 pixels. Predictions are consistently smooth and elliptical, addressing common failure modes of existing approaches. Ultra-ECP provides an effective and computationally lightweight pathway for adapting large vision models to fetal cardiac biometrics, enabling reliable and clinically practical semi-automated tools.

Primary Subject Area: Application: Radiology

Secondary Subject Area: Segmentation

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Submission Number: 192