# Standard Plane Detection in 3D Fetal Ultrasound Using an Iterative Transformation Network

arXiv: https://arxiv.org/abs/1806.07486 · HTML: https://mlsanity.com/paper/1806.07486/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

## Built on by (most cited first, 15 of 15)

- [A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis](https://mlsanity.com/paper/2201.12260/) (2022)
- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries](https://mlsanity.com/paper/2209.09610/) (2022)
- [Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2103.14502/) (2021)
- [Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2105.10626/) (2021)
- [Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework](https://mlsanity.com/paper/2111.02167/) (2021)
- [Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos](https://mlsanity.com/paper/2405.13235/) (2024)
- [Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound](https://mlsanity.com/paper/2207.00475/) (2022)
- [Automatic linear measurements of the fetal brain on MRI with deep neural networks](https://mlsanity.com/paper/2106.08174/) (2021)
- [Uncertainty-aware Diffusion and Reinforcement Learning for Joint Plane Localization and Anomaly Diagnosis in 3D Ultrasound](https://mlsanity.com/paper/2506.23538/) (2025)
- [Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes](https://mlsanity.com/paper/2109.10731/) (2021)
- [Echo-POSED: Geometric Self-Distillation for Echocardiography Guidance](https://mlsanity.com/paper/2606.02634/) (2026)
- [A Cognitive Explainer for Fetal ultrasound images classifier Based on Medical Concepts](https://mlsanity.com/paper/2201.07798/) (2022)
- [Measuring proximity to standard planes during fetal brain ultrasound scanning](https://mlsanity.com/paper/2404.07124/) (2024)
- [AWR-Net: Decoupling Anatomy and Appearance for 3D Fetal Brain Ultrasound Synthesis](https://mlsanity.com/paper/2609.22635/) (2026)

## Abstract (arXiv)

Standard scan plane detection in fetal brain ultrasound (US) forms a crucial step in the assessment of fetal development. In clinical settings, this is done by manually manoeuvring a 2D probe to the desired scan plane. With the advent of 3D US, the entire fetal brain volume containing these standard planes can be easily acquired. However, manual standard plane identification in 3D volume is labour-intensive and requires expert knowledge of fetal anatomy. We propose a new Iterative Transformation Network (ITN) for the automatic detection of standard planes in 3D volumes. ITN uses a convolutional neural network to learn the relationship between a 2D plane image and the transformation parameters required to move that plane towards the location/orientation of the standard plane in the 3D volume. During inference, the current plane image is passed iteratively to the network until it converges to the standard plane location. We explore the effect of using different transformation representations as regression outputs of ITN. Under a multi-task learning framework, we introduce additional classification probability outputs to the network to act as confidence measures for the regressed transformation parameters in order to further improve the localisation accuracy. When evaluated on 72 US volumes of fetal brain, our method achieves an error of 3.83mm/12.7 degrees and 3.80mm/12.6 degrees for the transventricular and transcerebellar planes respectively and takes 0.46s per plane. Source code is publicly available at https://github.com/yuanwei1989/plane-detection.