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Medical-image Analysis and Statistical Interpretation Lab

Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

Posted by on Monday, October 31, 2016 in Image Segmentation, Neuroimaging.

Yuankai Huo, Andrew J. Asman, Andrew J. Plassard, Bennett A. Landman. “Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion.” Human Brain Mapping. In Press October 2016

Full text: https://www.ncbi.nlm.nih.gov/pubmed/27726243

Abstract

Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of brain tissues. None of the three provide explicit skull/CSF boundary (SCB) since it is challenging to distinguish these dark structures in a T1-weighted image. However, explicit SCB not only leads to a natural way of obtaining TICV (i.e., counting voxels inside the skull) but also allows sub-definition of TICV, for example, the posterior fossa volume (PFV). In this article, they proposed to use multi-atlas label fusion to obtain TICV and PFV simultaneously. The main contributions are: (1) TICV and PFV are simultaneously obtained with explicit SCB from a single T1-weighted image. (2) TICV and PFV labels are added to the widely used BrainCOLOR atlases. (3) Detailed mathematical derivation of non-local spatial STAPLE (NLSS) label fusion is presented. As the skull is clearly distinguished in CT images, we use a semi-manual procedure to obtain atlases with TICV and PFV labels using 20 subjects who both have a MR and CT scan. The proposed method provides simultaneous TICV and PFV estimation while achieving more accurate TICV estimation compared with FreeSurfer, FSL, SPM12, and the previously proposed STAPLE based approach. The newly developed TICV and PFV labels for the OASIS BrainCOLOR atlases provide acceptable performance, which enables simultaneous TICV and PFV estimation during whole brain segmentation. The NLSS method and the new atlases have been made freely available. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

Qualitative results comparing multi-atlas segmentation methods with semi-manual segmentation. The red contours represent the spatial location of the semi-manual segmentation. The white color indicates the negative error, in which the estimated segmentation is smaller than the semi-manual reference. The green and purple color outside the red contours indicates the positive error, in which the estimated segmentation is larger than reference.
Qualitative results comparing multi-atlas segmentation methods with semi-manual segmentation. The red contours represent the spatial location of the semi-manual segmentation. The white color indicates the negative error, in which the estimated segmentation is smaller than the semi-manual reference. The green and purple color outside the red contours indicates the positive error, in which the estimated segmentation is larger than reference.

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