Abstract
| Original language | English |
|---|---|
| Title of host publication | 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI) |
| Publisher | Institute of Electrical and Electronic Engineers IEEE |
| Pages | 896-899 |
| ISBN (Electronic) | 978-1-4577-1858-8, 978-1-4577-1856-4 |
| ISBN (Print) | 978-1-4577-1857-1 |
| DOIs | |
| Publication status | Published - 2012 |
| MoE publication type | A4 Article in a conference publication |
| Event | 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain Duration: 2 May 2012 → 5 May 2012 Conference number: 9 |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 1945-7928 |
| ISSN (Electronic) | 1945-8452 |
Conference
| Conference | 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 |
|---|---|
| Abbreviated title | ISBI 2012 |
| Country | Spain |
| City | Barcelona |
| Period | 2/05/12 → 5/05/12 |
Fingerprint
Keywords
- brain atrophy
- hippocampus
- Alzheimers disease
Cite this
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Multi-class brain segmentation using atlas propagation and EM-based refinement. / Ledig, Christian; Wolz, Robin; Aljabar, Paul; Lötjönen, Jyrki; Heckemann, Rolf; Hammers, Alex; Rueckert, Daniel.
2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). Institute of Electrical and Electronic Engineers IEEE, 2012. p. 896-899.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review
TY - GEN
T1 - Multi-class brain segmentation using atlas propagation and EM-based refinement
AU - Ledig, Christian
AU - Wolz, Robin
AU - Aljabar, Paul
AU - Lötjönen, Jyrki
AU - Heckemann, Rolf
AU - Hammers, Alex
AU - Rueckert, Daniel
N1 - Project: 18493 Project : 71992
PY - 2012
Y1 - 2012
N2 - In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical structures such as the hippocampus. In this work we extend a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model. In particular we improve the segmentation result of a locally weighted multi-atlas fusion method for 41 different structures simultaneously by applying a subsequent EM optimization step. We evaluate the proposed approach on 30 manually annotated brain MR images and observe an improvement of label overlaps to a manual reference by up to 6%. We also achieved a considerably improved group separation when the proposed segmentation framework is applied to a volumetric analysis of 404 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
AB - In recent years, multi-atlas segmentation has emerged as one of the most accurate techniques for the segmentation of brain magnetic resonance (MR) images, especially when combined with intensity-based refinement techniques such as graph-cut or expectation-maximization (EM) optimization. However, most of the work so far has focused on intensity-based refinement strategies for individual anatomical structures such as the hippocampus. In this work we extend a previously proposed framework for labeling whole brain scans by incorporating a global and stationary Markov random field that ensures the consistency of the neighbourhood relations between structures with an a-priori defined model. In particular we improve the segmentation result of a locally weighted multi-atlas fusion method for 41 different structures simultaneously by applying a subsequent EM optimization step. We evaluate the proposed approach on 30 manually annotated brain MR images and observe an improvement of label overlaps to a manual reference by up to 6%. We also achieved a considerably improved group separation when the proposed segmentation framework is applied to a volumetric analysis of 404 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.
KW - brain atrophy
KW - hippocampus
KW - Alzheimers disease
U2 - 10.1109/ISBI.2012.6235693
DO - 10.1109/ISBI.2012.6235693
M3 - Conference article in proceedings
SN - 978-1-4577-1857-1
SP - 896
EP - 899
BT - 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)
PB - Institute of Electrical and Electronic Engineers IEEE
ER -