Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings

TL;DR

This paper introduces FetalCLIP$_{CLS}$, a vision-language model fine-tuned for fetal ultrasound image quality assessment, demonstrating improved accuracy and potential to enhance prenatal care in low-resource settings.

Contribution

It presents a novel adaptation of FetalCLIP using Low-Rank Adaptation for automated fetal ultrasound IQA, outperforming existing CNN and Transformer baselines.

Findings

FetalCLIP$_{CLS}$ achieves a highest F1 score of 0.757.

Re-purposed segmentation model improves F1 score to 0.771.

Parameter-efficient fine-tuning enhances task-specific performance.

Abstract

Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements heavily depends on the expertise of sonographers, posing a significant challenge in low-income countries due to the scarcity of trained personnel. To address this issue, we leverage FetalCLIP, a vision-language model pretrained on a curated dataset of over 210,000 fetal ultrasound image-caption pairs, to perform automated fetal ultrasound image quality assessment (IQA) on blind-sweep ultrasound data. We introduce FetalCLIP$_{CLS}$, an IQA model adapted from FetalCLIP using Low-Rank Adaptation (LoRA), and evaluate it on the ACOUSLIC-AI dataset against six CNN and Transformer baselines. FetalCLIP$_{CLS}$ achieves the highest F1 score of 0.757. Moreover, we show that an adapted segmentation model, when repurposed for classification, further improves performance, achieving an F1 score of 0.771. Our work demonstrates how parameter-efficient fine-tuning of fetal ultrasound foundation models can enable task-specific adaptations, advancing prenatal care in resource-limited settings. The experimental code is available at: https://github.com/donglihe-hub/FetalCLIP-IQA.