Abstract
Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8. months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6. months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889 ± 0.008 for white matter and 0.870 ± 0.006 for gray matter.
| Original language | English |
|---|---|
| Pages (from-to) | 152-164 |
| Number of pages | 13 |
| Journal | NeuroImage |
| Volume | 89 |
| DOIs | |
| Publication status | Published - 2014 Apr 1 |
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Keywords
- Anatomical constraint
- Infant brain images
- Isointense stage
- Low contrast
- Sparse representation
- Tissue segmentation
ASJC Scopus subject areas
- Cognitive Neuroscience
- Neurology
Cite this
Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation. / Wang, Li; Shi, Feng; Gao, Yaozong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang.
In: NeuroImage, Vol. 89, 01.04.2014, p. 152-164.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation
AU - Wang, Li
AU - Shi, Feng
AU - Gao, Yaozong
AU - Li, Gang
AU - Gilmore, John H.
AU - Lin, Weili
AU - Shen, Dinggang
PY - 2014/4/1
Y1 - 2014/4/1
N2 - Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8. months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6. months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889 ± 0.008 for white matter and 0.870 ± 0.006 for gray matter.
AB - Segmentation of infant brain MR images is challenging due to poor spatial resolution, severe partial volume effect, and the ongoing maturation and myelination processes. During the first year of life, the brain image contrast between white and gray matters undergoes dramatic changes. In particular, the image contrast inverses around 6-8. months of age, where the white and gray matter tissues are isointense in T1 and T2 weighted images and hence exhibit the extremely low tissue contrast, posing significant challenges for automated segmentation. In this paper, we propose a general framework that adopts sparse representation to fuse the multi-modality image information and further incorporate the anatomical constraints for brain tissue segmentation. Specifically, we first derive an initial segmentation from a library of aligned images with ground-truth segmentations by using sparse representation in a patch-based fashion for the multi-modality T1, T2 and FA images. The segmentation result is further iteratively refined by integration of the anatomical constraint. The proposed method was evaluated on 22 infant brain MR images acquired at around 6. months of age by using a leave-one-out cross-validation, as well as other 10 unseen testing subjects. Our method achieved a high accuracy for the Dice ratios that measure the volume overlap between automated and manual segmentations, i.e., 0.889 ± 0.008 for white matter and 0.870 ± 0.006 for gray matter.
KW - Anatomical constraint
KW - Infant brain images
KW - Isointense stage
KW - Low contrast
KW - Sparse representation
KW - Tissue segmentation
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U2 - 10.1016/j.neuroimage.2013.11.040
DO - 10.1016/j.neuroimage.2013.11.040
M3 - Article
C2 - 24291615
AN - SCOPUS:84892862607
VL - 89
SP - 152
EP - 164
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -