FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images

Fetal crown-rump length (CRL) is one of the most accurate method for estimating gestational age in early pregnancy. Typically, the process of manual CRL measurement by physicians is cumbersome, prone to errors due to fetal position, and susceptible to inter-observer variability. To provide an accurate, real-time, and reliable fetal CRL measurement solution, we propose FastCRL that utilizes key landmarks detection for efficient CRL measurements and fetal position estimation. Specifically, fast and lightweight network blocks are employed for both the encoder and decoder. By outputting low-resolution heatmaps and axial offset maps of key landmarks, we achieve a balance between high accuracy and fast inference speed. A novel Lightweight Adaptive Fourier Transform (LAFT) module is introduced to globally filter noise in ultrasound images and enhance the features required for landmark prediction. Additionally, the challenge of evaluating fetal position flexion and extension is effectively addressed by analyzing the angles between key landmarks on the fetal head, buttocks, and neck. The experimental results on our dataset indicate that our method for determining fetal position is both objective and efficient. FastCRL achieves a performance level consistent with the average human expert. In terms of measuring CRL, FastCRL achieved an error rate of less than 3% in 99.1% of measurements with 32 ms latency, significantly outperforming other baselines and demonstrating substantial potential for clinical application.