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Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts
van der Lijn F, den Heijer T, Breteler MM, Niessen WJ.. NeuroImage, 2008-08-12
Topic: Risk, prevention and care in population studies
Since hippocampal volume has been found to be an early biomarker for Alzheimer's disease, there is large interest in automated methods to accurately, robustly, and reproducibly extract the hippocampus from MRI data. In this work we present a segmentation method based on the minimization of an energy functional with intensity and prior terms, which are derived from manually labelled training images. The intensity energy is based on a statistical intensity model that is learned from the training images. The prior energy consists of a spatial and regularity term. The spatial prior is obtained from a probabilistic atlas created by registering the training images to the unlabelled target image, and deforming and averaging the training labels. The regularity prior energy encourages smooth segmentations. The resulting energy functional is globally minimized using graph cuts. The method was evaluated using image data from a population-based study on diseases among the elderly. Two set of images were used: a small set of 20 manually labelled MR images and a larger set of 498 images, for which manual volume measurements were available, but no segmentations. This data was previously used in a volumetry study that found significant associations between hippocampal volume and cognitive decline and incidence of dementia. Cross-validation experiments with the labelled set showed similarity indices of 0.852 and 0.864 and mean surface distances of 0.40 and 0.36 mm for the left and right hippocampus. 83% of the automated segmentations of the large set were rated as 'good' by a trained observer. Also, the proposed method was used to repeat the manual hippocampal volumetry study. The automatically obtained hippocampal volumes showed significant associations with cognitive decline and dementia, similar to the manually measured volumes. Finally, direct quantitative and qualitative comparisons showed that the proposed method outperforms a multi-atlas based segmentation method.
Key findings
- This data was previously used in a volumetry study that found significant associations between hippocampal volume and cognitive decline and incidence of dementia.
- The automatically obtained hippocampal volumes showed significant associations with cognitive decline and dementia, similar to the manually measured volumes.
- Finally, direct quantitative and qualitative comparisons showed that the proposed method outperforms a multi-atlas based segmentation method.
Study details
- Evidence strengthLow
How this study scored 36/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 21 of the 58 points we could judge it on, which is 36 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2008 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 18761411) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 21 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 21 of the 58 points we could judge it on is 36 out of 100. |
| Total | 36 / 100 | — |
What each of these 20 components measures, and what this score is not →
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APA 7th
van der Lijn, F., den Heijer, T., Breteler, M. M., & Niessen, W. J. (2008). Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. NeuroImage. https://doi.org/10.1016/j.neuroimage.2008.07.058
Vancouver
van der Lijn F, den Heijer T, Breteler MM, Niessen WJ. Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. NeuroImage. 2008. doi:10.1016/j.neuroimage.2008.07.058. PMID: 18761411.
BibTeX
@article{vanderlijn2008-18761411,
author = {van der Lijn, F. and den Heijer, T. and Breteler, M. M. and Niessen, W. J.},
title = {{Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts}},
journal = {NeuroImage},
year = {2008},
doi = {10.1016/j.neuroimage.2008.07.058},
pmid = {18761411},
url = {https://doi.org/10.1016/j.neuroimage.2008.07.058}
}
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