Reliability of tissue volumes and their spatial distribution for segmented magnetic resonance images
Introduction
Use of structural magnetic resonance images (MRIs) to guide analysis of functional images (e.g. PET, SPECT, fMRI) or data from other imaging modalities (e.g. magnetization transfer imaging, MR spectroscopy, diffusion weighted imaging) is becoming increasingly popular. Anatomic regions of interest (ROIs) can be better delineated on high resolution structural MRIs than on lower resolution functional and other images, and these ROIs can then be used to guide quantitative analysis of the lower resolution images (e.g. Migneco et al., 1994, Mountz et al., 1994, Weiner et al., 1998). Tissue segmentation from the structural MRIs can be used to correct the lower resolution images for atrophic and differential tissue compartment contribution effects (Meltzer et al., 1990, Meltzer et al., 1996a, Meltzer et al., 1996b, Müller-Gärtner et al., 1992, Weiner et al., 1998).
MRI segmentation is most often used to assess tissue specific volumes as measures of atrophy or as differences in brain organization between diagnostic groups. The validity of the segmentation tissue classification can only be assessed against a true gold standard. Investigators have assessed the reliability of MRI segmentation by evaluating the repeatability of the resultant tissue volume measurements (Bonar et al., 1993, Byrum et al., 1996, Cohen et al., 1992, Fisher et al., 1997, Harris et al., 1999, Kikinis et al., 1992, Reiss et al., 1998). Sources of unreliability of MRI segmentation volumetric measures identified by these investigators include intra- and inter-operator variability, imperfections in data acquisition (RF inhomogeneity, motion and flow artifacts), drift in imager function over time, and partial volume effects (PVE). When segmented structural MRIs are used to guide the analysis of functional and other images, their utility depends on the validity and reliability of the segmentation on a pixel-by-pixel basis. In these cases, it is not just the volume of cortical gray matter that is important, but it is the spatial location of the gray matter voxels in the brain that is used to guide the analysis of the other imaging modality. We have found no publications assessing the reliability of segmentation algorithms on a pixel-by-pixel basis.
It is difficult to validate tissue segmentation algorithms using data acquired in vivo, since there is no way of determining the true tissue classifications of each MRI voxel. Manual segmentation by an expert is often used as a gold standard for validating segmentation algorithms (Harris et al., 1999), but such efforts are hindered by intra- and inter-rater variability. Investigators at the Montreal Neurologic Institute (MNI) (Collins et al., 1998, Kwan et al., 1996) have developed a realistic digital brain phantom and MRI simulator, which can be used to evaluate image-processing methods. Using a web interface (), differently weighted simulated MRIs can be downloaded and used to test segmentation algorithms. The output of segmentation can be compared to the digital brain phantom to compute an objective measure of performance.
This report examines the validity of our K-means clustering segmentation approach by applying it to MRI phantom data, and then focuses on the reproducibility or reliability of the volumetric measures and of the spatial distribution of tissue categories in serially collected, segmented, anatomic images. Only by comparing the reproducibility of the spatial distribution of tissue categories across repeat imaging studies can the utility of segmentation for voxel-by-voxel co-analysis of functional images be assessed.
Section snippets
McGill University brainweb images
We obtained simulated MRIs from , using their normal brain model. All images had 1-mm slice thickness with 1×1 mm2 in-plane resolution. We obtained T1, PD, and T2 weighted images, with the following parameters: T1 (simulated 3D spoiled FLASH TR/TE, FA — 18/10, 30°), 3% noise level, 0% inhomogeneity; PD (simulated early echo from 2D multislice dual spin echo TR/TE — 3300/35), 3% noise level, 20% inhomogeneity, T2 (simulated late echo from 2D multislice dual
Results
Table 1 shows the volumes obtained from the discrete anatomical brain phantom for gray matter, white matter, and CSF compared to the volumes output by the segmentation of the T1-, PD-, and T2-weighted images generated by the MRI simulator based on the discrete anatomical brain phantom. The percentage difference between these volumes were all less than 5%, and the overlaps were 0.94 or greater. Fig. 4 shows slices from the discrete anatomical brain phantom and the corresponding slices from the
Discussion
Our K-means clustering segmentation method, which utilizes intensity information from T1-, PD-, and T2-weighted images, performed extremely well on the data generated by MNI using their realistic digital brain phantom and MRI simulator. Our method produces a classification into gray matter, white matter, and CSF, and the tissue volume differences between the segmentation image and ‘truth’ were less than 5% for these three tissues. These tissue volume differences are similar to those for other
Acknowledgements
The authors would like to thank Diana Truran, Alanna McAlorum, Rosanna Jeremias, and Dawn Hardin for their assistance in recruiting subjects, running the magnet, and processing the MRIs. This work was supported by NIA grant AG12435, NIAAA grant P01AA11493, NIDA grant R01DA08365 and a DVA Research Career Scientist Award (George Fein).
References (25)
- et al.
Accuracy and reproducibility of brain and tissue volumes using a magnetic resonance segmentation method
Psychiatry Research: Neuroimaging
(1996) - et al.
Segmentation techniques for the classification of brain tissue using magnetic resonance imaging
Psychiatry Research: Neuroimaging
(1992) - et al.
Computerized localization of brain structures in single photon emission computed tomography using a proportional anatomical stereotactic atlas
Computerized Medical Imaging and Graphics
(1994) - et al.
A reference method for correlation of anatomic and functional brain images: validation and clinical application
Seminars in Nuclear Medicine
(1994) - et al.
A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images
Magnetic Resonance Imaging
(1999) - et al.
Graphical analysis of MR feature space for measurement of CSF, gray-matter, and white-matter volumes
Journal of Computer Assisted Tomography
(1993) - et al.
Design and construction of a realistic digital brain phantom
IEEE Transactions on Medical Imaging
(1998) - et al.
Knowledge-based 3D segmentation of the brain in MR images for quantitative multiple sclerosis lesion tracking
- et al.
Improving tissue classification in MRI: a three-dimensional multispectral discriminant analysis method with automated training class selection
Journal of Computer Assisted Tomography
(1999) - et al.
Robust multimodality registration for brain mapping
Human Brain Mapping
(1997)
Methods for measuring brain morphologic features on magnetic resonance images
Archives of Neurology
Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging
Journal of Magnetic Resonance Imaging
Cited by (40)
Brain tissue volumes in the general population of the elderly. The AGES-Reykjavik Study.
2012, NeuroImageCitation Excerpt :It has been suggested that overlaps are generally better for tissue classes with larger volumes and/or tissue classes that are contiguous with many interior voxels compared to boundary voxels. The underlying reason for this is that larger volumes with high overlaps will have relatively fewer partial-volume voxels than small regions (Cardenas et al., 2001). We believe this explains the relatively lower similarity index for WMH (0.62), compared to the other tissue classes (0.82, 0.82 and 0.83 for GM, NWM and CSF respectively).
Neuroimaging in Psychiatry
2009, Neurologic ClinicsCitation Excerpt :Relationships with liver function, cytokines, nutritional status, and hormone levels, however, are poor. By using deformation-based morphometric MRI, studies have demonstrated those patients able to maintain abstinence had significant tissue volume recovery in the frontal, parietal, and temporal lobes and in the thalamus, brainstem, corpus callosum, anterior cingulated, insula, and subcortical white matter.6 Findings for light drinkers were less pronounced.
Brain tissue volumes in the general elderly population. The Rotterdam Scan Study
2008, Neurobiology of AgingCitation Excerpt :This was particularly due to low similarity index for small WML. The underlying reason is that the same amount of partial-volume voxels being classified differently will have a larger effect on the similarity index of smaller WML than on the similarity index of larger WML (Cardenas et al., 2001). Indeed, if we excluded two persons with smallest WML from our validation set, the similarity index for WML increased to 0.71.
A new improved version of the realistic digital brain phantom
2006, NeuroImage