# Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning

arXiv: https://arxiv.org/abs/1909.00273 · HTML: https://mlsanity.com/paper/1909.00273/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning](https://mlsanity.com/paper/2204.11433/) (2022)
- [AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes](https://mlsanity.com/paper/2107.05255/) (2021)
- [FPUS23: An Ultrasound Fetus Phantom Dataset with Deep Neural Network Evaluations for Fetus Orientations, Fetal Planes, and Anatomical Features](https://mlsanity.com/paper/2303.07852/) (2023)
- [RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm](https://mlsanity.com/paper/2301.02807/) (2023)
- [A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry](https://mlsanity.com/paper/2108.12719/) (2021)
- [Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images](https://mlsanity.com/paper/2201.07935/) (2022)
- [FUSQA: Fetal Ultrasound Segmentation Quality Assessment](https://mlsanity.com/paper/2303.04418/) (2023)
- [FFPN: Fourier Feature Pyramid Network for Ultrasound Image Segmentation](https://mlsanity.com/paper/2308.13790/) (2023)
- [Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation](https://mlsanity.com/paper/2112.14320/) (2021)
- [U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation](https://mlsanity.com/paper/2411.16890/) (2024)
- [Towards Objective Obstetric Ultrasound Assessment: Contrastive Representation Learning for Fetal Movement Detection](https://mlsanity.com/paper/2510.20214/) (2025)
- [Towards Better Ultrasound Video Segmentation Foundation Model: An Empirical study on SAM2 Finetuning from Data Perspective](https://mlsanity.com/paper/2511.05731/) (2025)

## Abstract (arXiv)

Ultrasound imaging is a standard examination during pregnancy that can be used for measuring specific biometric parameters towards prenatal diagnosis and estimating gestational age. Fetal head circumference (HC) is one of the significant factors to determine the fetus growth and health. In this paper, a multi-task deep convolutional neural network is proposed for automatic segmentation and estimation of HC ellipse by minimizing a compound cost function composed of segmentation dice score and MSE of ellipse parameters. Experimental results on fetus ultrasound dataset in different trimesters of pregnancy show that the segmentation results and the extracted HC match well with the radiologist annotations. The obtained dice scores of the fetal head segmentation and the accuracy of HC evaluations are comparable to the state-of-the-art.