# Factorisation-based Image Labelling

arXiv: https://arxiv.org/abs/2111.10326 · HTML: https://mlsanity.com/paper/2111.10326/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Conditional Random Fields as Recurrent Neural Networks](https://mlsanity.com/paper/1502.03240/) (2015)
- [3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles](https://mlsanity.com/paper/1903.12152/) (2019)
- [QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy](https://mlsanity.com/paper/1801.04161/) (2018)
- [Automatic segmentation of MR brain images with a convolutional neural network](https://mlsanity.com/paper/1704.03295/) (2017)
- [DeepNAT: Deep Convolutional Neural Network for Segmenting Neuroanatomy](https://mlsanity.com/paper/1702.08192/) (2017)
- [Error Corrective Boosting for Learning Fully Convolutional Networks with Limited Data](https://mlsanity.com/paper/1705.00938/) (2017)
- [Deep Neural Networks for Anatomical Brain Segmentation](https://mlsanity.com/paper/1502.02445/) (2015)
- [End-to-end semantic segmentation of personalized deep brain structures for non-invasive brain stimulation](https://mlsanity.com/paper/2002.05487/) (2020)
- [Flexible Bayesian Modelling for Nonlinear Image Registration](https://mlsanity.com/paper/2006.02338/) (2020)
- [Nonlinear Markov Random Fields Learned via Backpropagation](https://mlsanity.com/paper/1902.10747/) (2019)

## 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.