mlsanityData to 7 Oct 2026

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

arXiv 2110.04140 ↗First posted 8 Oct 2021cs.CV eess.IV physics.med-phcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 8 of its 65 references that are in the map. First posted on arXiv 8 Oct 2021.

Builds on

8 of 65 references.

References · 8

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016DeepCut: Object Segmentation from Bounding Box Annotations using Convolutional Neural Networkscited by 79
  3. 2018Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registrationcited by 40
  4. 2019Semi-Supervised and Task-Driven Data Augmentationcited by 40
  5. 20173D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Imagescited by 14
  6. 2017Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learningcited by 13
  7. 2019Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Networkcited by 9
  8. 2017Automatic segmentation of the intracranialvolume in fetal MR imagescited by 3

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4 papers.