mlsanityData to 13 Oct 2026

Meta-Learning Initializations for Interactive Medical Image Registration

arXiv 2210.15371 ↗First posted 27 Oct 2022eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ultrasound (TRUS) images. Our approach obtains comparable registration error (4.26 mm) to the best-performing non-interactive learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the data, and occurring in real-time during acquisition. Applying sparsely sampled data to non-interactive methods yields higher registration errors (6.26 mm), demonstrating the effectiveness of interactive MR-TRUS registration, which may be applied intraoperatively given the real-time nature of the adaptation process.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 32 of its 71 references that are in the map. First posted on arXiv 27 Oct 2022.

Builds on

32 of 71 references.

References · 32

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20 more
  1. 2017DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentationcited by 110
  2. 2017Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuningcited by 110
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  20. 2022Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registrationcited by 3

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyondcited by 55
  2. 2024A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trendscited by 16
  3. 2023Meta-learning in healthcare: A surveycited by 9
  4. 2024A Narrative Review of Image Processing Techniques Related to Prostate Ultrasoundcited by 3