# FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements

arXiv: https://arxiv.org/abs/2107.06943 · HTML: https://mlsanity.com/paper/2107.06943/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 14 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Y-Net: Joint Segmentation and Classification for Diagnosis of Breast Biopsy Images](https://mlsanity.com/paper/1806.01313/) (2018)
- [Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1804.09102/) (2018)
- [Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN](https://mlsanity.com/paper/1806.09766/) (2018)
- [Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images](https://mlsanity.com/paper/1706.03702/) (2017)
- [Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers](https://mlsanity.com/paper/1908.02582/) (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)
- [Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries](https://mlsanity.com/paper/2209.09610/) (2022)
- [BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video](https://mlsanity.com/paper/2205.09382/) (2022)
- [Deep Learning Fetal Ultrasound Video Model Match Human Observers in Biometric Measurements](https://mlsanity.com/paper/2205.13835/) (2022)
- [A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry](https://mlsanity.com/paper/2512.16710/) (2025)
- [FUSQA: Fetal Ultrasound Segmentation Quality Assessment](https://mlsanity.com/paper/2303.04418/) (2023)
- [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)
- [Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound](https://mlsanity.com/paper/2507.00398/) (2025)
- [POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 Detection](https://mlsanity.com/paper/2201.09360/) (2022)
- [Measuring proximity to standard planes during fetal brain ultrasound scanning](https://mlsanity.com/paper/2404.07124/) (2024)
- [Entropy-Guided Agreement-Diversity: A Semi-Supervised Active Learning Framework for Fetal Head Segmentation in Ultrasound](https://mlsanity.com/paper/2601.17460/) (2026)
- [A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies](https://mlsanity.com/paper/2606.19174/) (2026)

## Abstract (arXiv)

In this paper, we propose an end-to-end multi-task neural network called FetalNet with an attention mechanism and stacked module for spatio-temporal fetal ultrasound scan video analysis. Fetal biometric measurement is a standard examination during pregnancy used for the fetus growth monitoring and estimation of gestational age and fetal weight. The main goal in fetal ultrasound scan video analysis is to find proper standard planes to measure the fetal head, abdomen and femur. Due to natural high speckle noise and shadows in ultrasound data, medical expertise and sonographic experience are required to find the appropriate acquisition plane and perform accurate measurements of the fetus. In addition, existing computer-aided methods for fetal US biometric measurement address only one single image frame without considering temporal features. To address these shortcomings, we propose an end-to-end multi-task neural network for spatio-temporal ultrasound scan video analysis to simultaneously localize, classify and measure the fetal body parts. We propose a new encoder-decoder segmentation architecture that incorporates a classification branch. Additionally, we employ an attention mechanism with a stacked module to learn salient maps to suppress irrelevant US regions and efficient scan plane localization. We trained on the fetal ultrasound video comes from routine examinations of 700 different patients. Our method called FetalNet outperforms existing state-of-the-art methods in both classification and segmentation in fetal ultrasound video recordings.