Factorisation-based Image Labelling

by   Yu Yan, et al.

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.


page 15

page 23

page 27


Deep Neural Networks for Anatomical Brain Segmentation

We present a novel approach to automatically segment magnetic resonance ...

Segmentation of 2D Brain MR Images

Brain tumour segmentation is an essential task in medical image processi...

3D MRI brain tumor segmentation using autoencoder regularization

Automated segmentation of brain tumors from 3D magnetic resonance images...

CORPS: Cost-free Rigorous Pseudo-labeling based on Similarity-ranking for Brain MRI Segmentation

Segmentation of brain magnetic resonance images (MRI) is crucial for the...

Self-Supervised Ultrasound to MRI Fetal Brain Image Synthesis

Fetal brain magnetic resonance imaging (MRI) offers exquisite images of ...

A Dataset for Lane Instance Segmentation in Urban Environments

Autonomous vehicles require knowledge of the surrounding road layout, wh...

Weak labels and anatomical knowledge: making deep learning practical for intracranial aneurysm detection in TOF-MRA

Supervised segmentation algorithms yield state-of-the-art results for au...