Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation
Open Access
- 1 January 2013
- journal article
- Published by Frontiers Media SA in Frontiers in Neuroscience
- Vol. 7, 27
- https://doi.org/10.3389/fninf.2013.00027
Abstract
Label fusion based multi-atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. This technique transfers segmentations from expert-labeled images, called atlases, to a novel image using deformable image registration. Errors produced by label transfer are further reduced by label fusion that combines the results produced by all atlases into a consensus solution. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity is a simple and highly effective label fusion technique. However, one limitation of most weighted voting methods is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this problem, we recently developed the joint label fusion technique and the corrective learning technique, which won the first place of the 2012 MICCAI Multi-Atlas Labeling Challenge and was one of the top performers in 2013 MICCAI Segmentation: Algorithms, Theory and Applications (SATA) challenge. To make our techniques more accessible to the scientific research community, we describe an Insight-Toolkit based open source implementation of our label fusion methods. Our implementation extends our methods to work with multi-modality imaging data and is more suitable for segmentation problems with multiple labels. We demonstrate the usage of our tools through applying them to the 2012 MICCAI Multi-Atlas Labeling Challenge brain image dataset and the 2013 SATA challenge canine leg image dataset. We report the best results on these two datasets so far.Keywords
This publication has 19 references indexed in Scilit:
- Multi-atlas Segmentation with Robust Label Transfer and Label FusionLecture Notes in Computer Science, 2013
- Decision Forests for Tissue-Specific Segmentation of High-Grade Gliomas in Multi-channel MRLecture Notes in Computer Science, 2012
- A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentationNeuroImage, 2011
- Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentationNeuroImage, 2011
- Entangled Decision Forests and Their Application for Semantic Segmentation of CT ImagesLecture Notes in Computer Science, 2011
- Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRINeuroImage, 2010
- Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusionNeuroImage, 2010
- Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brainMedical Image Analysis, 2008
- Automatic anatomical brain MRI segmentation combining label propagation and decision fusionNeuroImage, 2006
- Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brainsNeuroImage, 2004