# Ultrasound Plane Pose Regression: Assessing Generalized Pose Coordinates in the Fetal Brain

arXiv: https://arxiv.org/abs/2301.08317 · HTML: https://mlsanity.com/paper/2301.08317/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 19 Jan 2023.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views](https://mlsanity.com/paper/1505.05641/) (2015)
- [SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound](https://mlsanity.com/paper/1612.05601/) (2016)
- [Viewpoints and Keypoints](https://mlsanity.com/paper/1411.6067/) (2014)
- [Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound](https://mlsanity.com/paper/2007.04480/) (2020)
- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [DeepReg: a deep learning toolkit for medical image registration](https://mlsanity.com/paper/2011.02580/) (2020)
- [3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images](https://mlsanity.com/paper/1709.06341/) (2017)
- [VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation](https://mlsanity.com/paper/2205.06676/) (2022)
- [Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion](https://mlsanity.com/paper/1702.08891/) (2017)
- [Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images](https://mlsanity.com/paper/2209.05477/) (2022)
- [DSR: Direct Simultaneous Registration for Multiple 3D Images](https://mlsanity.com/paper/2105.10087/) (2021)

## Built on by (most cited first, 3 of 3)

- [UltraGauss: Ultrafast Gaussian Reconstruction of 3D Ultrasound Volumes](https://mlsanity.com/paper/2505.05643/) (2025)
- [Automatic facial axes standardization of 3D fetal ultrasound images](https://mlsanity.com/paper/2409.02826/) (2024)
- [Measuring proximity to standard planes during fetal brain ultrasound scanning](https://mlsanity.com/paper/2404.07124/) (2024)

## Abstract (arXiv)

In obstetric ultrasound (US) scanning, the learner's ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a significant challenge in skill acquisition. We aim to build a US plane localization system for 3D visualization, training, and guidance without integrating additional sensors. This work builds on top of our previous work, which predicts the six-dimensional (6D) pose of arbitrarily oriented US planes slicing the fetal brain with respect to a normalized reference frame using a convolutional neural network (CNN) regression network. Here, we analyze in detail the assumptions of the normalized fetal brain reference frame and quantify its accuracy with respect to the acquisition of transventricular (TV) standard plane (SP) for fetal biometry. We investigate the impact of registration quality in the training and testing data and its subsequent effect on trained models. Finally, we introduce data augmentations and larger training sets that improve the results of our previous work, achieving median errors of 2.97 mm and 6.63 degrees for translation and rotation, respectively.