# Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1804.09102 · HTML: https://mlsanity.com/paper/1804.09102/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements](https://mlsanity.com/paper/2107.06943/) (2021)
- [To Ask or Not to Ask: Learning to Require Human Feedback](https://mlsanity.com/paper/2510.08314/) (2025)
- [EllipseNet: Anchor-Free Ellipse Detection for Automatic Cardiac Biometrics in Fetal Echocardiography](https://mlsanity.com/paper/2109.12474/) (2021)
- [Towards Realistic Ultrasound Fetal Brain Imaging Synthesis](https://mlsanity.com/paper/2304.03941/) (2023)
- [Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements](https://mlsanity.com/paper/2205.13835/) (2022)
- [Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound](https://mlsanity.com/paper/2407.15119/) (2024)
- [A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry](https://mlsanity.com/paper/2512.16710/) (2025)
- [OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms](https://mlsanity.com/paper/2208.05616/) (2022)
- [Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps](https://mlsanity.com/paper/2107.02643/) (2021)
- [Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images](https://mlsanity.com/paper/2201.07935/) (2022)
- [Whole-examination AI estimation of fetal biometrics from 20-week ultrasound scans](https://mlsanity.com/paper/2401.01201/) (2024)
- [Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models](https://mlsanity.com/paper/2408.03654/) (2024)
- [Towards A Device-Independent Deep Learning Approach for the Automated Segmentation of Sonographic Fetal Brain Structures: A Multi-Center and Multi-Device Validation](https://mlsanity.com/paper/2202.13553/) (2022)
- [AGE-US: automated gestational age estimation based on fetal ultrasound images](https://mlsanity.com/paper/2506.16256/) (2025)
- [Cross-Modal Ultrasound-MRI Learning for Fetal Brain Ventricular Volumetry and Abnormality Screening](https://mlsanity.com/paper/2608.14763/) (2026)

## Abstract (arXiv)

Measurement of head biometrics from fetal ultrasonography images is of key importance in monitoring the healthy development of fetuses. However, the accurate measurement of relevant anatomical structures is subject to large inter-observer variability in the clinic. To address this issue, an automated method utilizing Fully Convolutional Networks (FCN) is proposed to determine measurements of fetal head circumference (HC) and biparietal diameter (BPD). An FCN was trained on approximately 2000 2D ultrasound images of the head with annotations provided by 45 different sonographers during routine screening examinations to perform semantic segmentation of the head. An ellipse is fitted to the resulting segmentation contours to mimic the annotation typically produced by a sonographer. The model's performance was compared with inter-observer variability, where two experts manually annotated 100 test images. Mean absolute model-expert error was slightly better than inter-observer error for HC (1.99mm vs 2.16mm), and comparable for BPD (0.61mm vs 0.59mm), as well as Dice coefficient (0.980 vs 0.980). Our results demonstrate that the model performs at a level similar to a human expert, and learns to produce accurate predictions from a large dataset annotated by many sonographers. Additionally, measurements are generated in near real-time at 15fps on a GPU, which could speed up clinical workflow for both skilled and trainee sonographers.