# A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

arXiv: https://arxiv.org/abs/2201.12260 · HTML: https://mlsanity.com/paper/2201.12260/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 43 of its 196 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 30 of 43)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images](https://mlsanity.com/paper/1808.08114/) (2018)
- [What do different evaluation metrics tell us about saliency models?](https://mlsanity.com/paper/1604.03605/) (2016)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Human pose estimation via Convolutional Part Heatmap Regression](https://mlsanity.com/paper/1609.01743/) (2016)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2011.08018/) (2020)
- [Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review](https://mlsanity.com/paper/1812.08577/) (2018)
- [Human-level Performance On Automatic Head Biometrics In Fetal Ultrasound Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1804.09102/) (2018)
- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks](https://mlsanity.com/paper/1707.05392/) (2017)
- [Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network](https://mlsanity.com/paper/1806.07486/) (2018)
- [Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging](https://mlsanity.com/paper/2011.00739/) (2020)
- [AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes](https://mlsanity.com/paper/2107.05255/) (2021)
- [Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction](https://mlsanity.com/paper/2108.00274/) (2021)
- [Fetal Ultrasound Image Segmentation for Measuring Biometric Parameters Using Multi-Task Deep Learning](https://mlsanity.com/paper/1909.00273/) (2019)
- [Agent with Warm Start and Active Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/1910.04331/) (2019)
- [FetalNet: Multi-task Deep Learning Framework for Fetal Ultrasound Biometric Measurements](https://mlsanity.com/paper/2107.06943/) (2021)
- [Temporal HeartNet: Towards Human-Level Automatic Analysis of Fetal Cardiac Screening Video](https://mlsanity.com/paper/1707.00665/) (2017)
- [Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images](https://mlsanity.com/paper/1805.10376/) (2018)
- [Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers](https://mlsanity.com/paper/1908.02582/) (2019)
- [Ultrasound Video Summarization using Deep Reinforcement Learning](https://mlsanity.com/paper/2005.09531/) (2020)
- [Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2103.14502/) (2021)
- [Estimation of preterm birth markers with U-Net segmentation network](https://mlsanity.com/paper/1908.09148/) (2019)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2105.10626/) (2021)
- [Annotation-Efficient Learning for Medical Image Segmentation based on Noisy Pseudo Labels and Adversarial Learning](https://mlsanity.com/paper/2012.14584/) (2020)
- [Generalize Ultrasound Image Segmentation via Instant and Plug & Play Style Transfer](https://mlsanity.com/paper/2101.03711/) (2021)
- [Region Proposal Network with Graph Prior and IoU-Balance Loss for Landmark Detection in 3D Ultrasound](https://mlsanity.com/paper/2004.00207/) (2020)
- [Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening](https://mlsanity.com/paper/2008.06966/) (2020)
- [Spontaneous preterm birth prediction using convolutional neural networks](https://mlsanity.com/paper/2008.07000/) (2020)

## Built on by (most cited first, 29 of 29)

- [PSFHS Challenge Report: Pubic Symphysis and Fetal Head Segmentation from Intrapartum Ultrasound Images](https://mlsanity.com/paper/2409.10980/) (2024)
- [Towards Realistic Ultrasound Fetal Brain Imaging Synthesis](https://mlsanity.com/paper/2304.03941/) (2023)
- [FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesis](https://mlsanity.com/paper/2503.14906/) (2025)
- [Multi-Task Learning Approach for Unified Biometric Estimation from Fetal Ultrasound Anomaly Scans](https://mlsanity.com/paper/2311.09607/) (2023)
- [Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound](https://mlsanity.com/paper/2407.15119/) (2024)
- [Generative Diffusion Model Bootstraps Zero-shot Classification of Fetal Ultrasound Images In Underrepresented African Populations](https://mlsanity.com/paper/2407.20072/) (2024)
- [A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry](https://mlsanity.com/paper/2512.16710/) (2025)
- [Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images](https://mlsanity.com/paper/2407.08020/) (2024)
- [DSSAU-Net:U-Shaped Hybrid Network for Pubic Symphysis and Fetal Head Segmentation](https://mlsanity.com/paper/2506.03684/) (2025)
- [Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs](https://mlsanity.com/paper/2206.01731/) (2022)
- [Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation](https://mlsanity.com/paper/2510.12953/) (2025)
- [Challenging DINOv3 Foundation Model under Low Inter-Class Variability: A Case Study on Fetal Brain Ultrasound](https://mlsanity.com/paper/2511.01915/) (2025)
- [FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation](https://mlsanity.com/paper/2601.15572/) (2026)
- [Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos](https://mlsanity.com/paper/2602.12922/) (2026)
- [A Federated Learning Framework for Stenosis Detection](https://mlsanity.com/paper/2310.19445/) (2023)
- [Leveraging Self-Supervised Learning for Fetal Cardiac Planes Classification using Ultrasound Scan Videos](https://mlsanity.com/paper/2407.21738/) (2024)
- [U-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation](https://mlsanity.com/paper/2411.16890/) (2024)
- [Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification](https://mlsanity.com/paper/2506.08623/) (2025)
- [Fusing Radiomic Features with Deep Representations for Gestational Age Estimation in Fetal Ultrasound Images](https://mlsanity.com/paper/2506.20407/) (2025)
- [Multi-Contrast Fusion Module: An attention mechanism integrating multi-contrast features for fetal torso plane classification](https://mlsanity.com/paper/2508.09644/) (2025)
- [Diagnostic Performance of Universal-Learning Ultrasound AI Across Multiple Organs and Tasks: the UUSIC25 Challenge](https://mlsanity.com/paper/2512.17279/) (2025)
- [Uncertainty-Calibrated Explainable Artificial Intelligence for Fetal Ultrasound Plane Classification: A Systematic Review](https://mlsanity.com/paper/2601.00990/) (2026)
- [FetalAgents: A Multi-Agent System for Fetal Ultrasound Image and Video Analysis](https://mlsanity.com/paper/2603.09733/) (2026)
- [Domain-Guided YOLO26 with Composite BCE-Dice-Lov'asz Loss for Multi-Class Fetal Head Ultrasound Segmentation](https://mlsanity.com/paper/2603.26755/) (2026)
- [Towards Reliable Fetal Ultrasound Interpretation with Multi-Agent Collaboration](https://mlsanity.com/paper/2605.25357/) (2026)
- [A Clinician-Centered Pipeline for Annotation and Evaluation in Ultrasound AI Studies](https://mlsanity.com/paper/2606.19174/) (2026)
- [FedCC: A Low-Resource Federated Adaptation of Foundation Models for Robust Corpus Callosum localization in Fetal Ultrasound Images](https://mlsanity.com/paper/2607.18283/) (2026)
- [FUSEP: A Multi-Center Benchmark for Diverse Tasks in Early Pregnancy Fetal Ultrasound Screening](https://mlsanity.com/paper/2608.04766/) (2026)
- [Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learning](https://mlsanity.com/paper/2609.31376/) (2026)

## Abstract (arXiv)

Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite a large number of survey papers already present in this field, most of them are focusing on a broader area of medical-image analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented on from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis, and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.