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Factorisation-based Image Labelling

arXiv 2111.10326 ↗First posted 19 Nov 2021cs.CVcited by 0

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Abstract (arXiv)

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based label propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 54 references that are in the map. First posted on arXiv 19 Nov 2021.

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References · 11

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Conditional Random Fields as Recurrent Neural Networkscited by 213
  3. 20193D Whole Brain Segmentation using Spatially Localized Atlas Network Tilescited by 50
  4. 2018QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomycited by 37
  5. 2017Automatic segmentation of MR brain images with a convolutional neural networkcited by 26
  6. 2017DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomycited by 25
  7. 2017Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Datacited by 12
  8. 2015Deep Neural Networks for Anatomical Brain Segmentationcited by 11
  9. 2020End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulationcited by 4
  10. 2020Flexible Bayesian Modelling for Nonlinear Image Registrationcited by 4
  11. 2019Nonlinear Markov Random Fields Learned via Backpropagationcited by 2

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