# Rapid head-pose detection for automated slice prescription of fetal-brain MRI

arXiv: https://arxiv.org/abs/2110.04140 · HTML: https://mlsanity.com/paper/2110.04140/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 65 references that are in the map.
- First posted on arXiv 8 Oct 2021.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networks](https://mlsanity.com/paper/1605.07866/) (2016)
- [Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration](https://mlsanity.com/paper/1803.05982/) (2018)
- [Semi-Supervised and Task-Driven Data Augmentation](https://mlsanity.com/paper/1902.05396/) (2019)
- [3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images](https://mlsanity.com/paper/1709.06341/) (2017)
- [Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning](https://mlsanity.com/paper/1710.09338/) (2017)
- [Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network](https://mlsanity.com/paper/1907.04500/) (2019)
- [Automatic segmentation of the intracranialvolume in fetal MR images](https://mlsanity.com/paper/1708.02282/) (2017)

## Built on by (most cited first, 4 of 4)

- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)
- [Anatomy-aware and acquisition-agnostic joint registration with SynthMorph](https://mlsanity.com/paper/2301.11329/) (2023)
- [Search Wide, Focus Deep: Automated Fetal Brain Extraction with Sparse Training Data](https://mlsanity.com/paper/2410.20532/) (2024)
- [Equivariant symmetry-aware head pose estimation for fetal MRI](https://mlsanity.com/paper/2512.04890/) (2025)

## Abstract (arXiv)

In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. Our ~2-second procedure automatically locates the fetal brain and eyes, which we derive from maximally stable extremal regions (MSERs). The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%. The pipeline may be used to automatically orient the anatomical sequence, removing the need to estimate the head pose from 2D views and reducing delays during which motion can occur.