The creation of a brain atlas for image guided neurosurgery using serial histological data

McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, 3801, University St., Montréal, Canada H3A 2B4.
NeuroImage (Impact Factor: 6.36). 05/2006; 30(2):359-76. DOI: 10.1016/j.neuroimage.2005.09.041
Source: PubMed


Digital and print brain atlases have been used with success to help in the planning of neurosurgical interventions. In this paper, a technique presented for the creation of a brain atlas of the basal ganglia and the thalamus derived from serial histological data. Photographs of coronal histological sections were digitized and anatomical structures were manually segmented. A slice-to-slice nonlinear registration technique was used to correct for spatial distortions introduced into the histological data set at the time of acquisition. Since the histological data were acquired without any anatomical reference (e.g., block-face imaging, post-mortem MRI), this registration technique was optimized to use an error metric which calculates a nonlinear transformation minimizing the mean distance between the segmented contours between adjacent pairs of slices in the data set. A voxel-by-voxel intensity correction field was also estimated for each slice to correct for lighting and staining inhomogeneity. The reconstructed three-dimensional (3D) histological volume can be viewed in transverse and sagittal directions in addition to the original coronal.

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    • "Methods used for aligning histology slices include global pixelbased methods [1] [2] [3], local search methods [4] [5], and features-based methods [6]. However, these approaches assume the same appearance of all images. "
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    ABSTRACT: We describe an automatic method for fast registration of images with very different appearances. The images are jointly segmented into a small number of classes, the segmented images are registered, and the process is repeated. The segmentation calculates feature vectors on superpixels and then it finds a softmax classifier maximizing mu-tual information between class labels in the two images. For speed, the registration considers a sparse set of rectangular neighborhoods on the interfaces between classes. A triangulation is created with spatial regularization handled by pairwise spring-like terms on the edges. The optimal transformation is found globally using loopy belief propagation. Multiresolution helps to improve speed and ro-bustness. Our main application is registering stained histological slices, which are large and differ both in the local and global appear-ance. We show that our method has comparable accuracy to standard pixel-based registration, while being faster and more general.
    International Symposium on Biomedical Imaging, IEEE, Beijing, China; 05/2014
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    • "The latter introduces non-linear tissue distortions, such as separation, missing parts, squashing, stretching, folding and tearing, as well as artifacts including air bubbles and dust (Ju et al., 2006). Intensity inhomogeneity within and across sections caused by inconsistent staining and uneven illumination are other common issues (Malandain and Bardinet, 2003; Malandain et al., 2004; Chakravarty et al., 2006). These problems not only limit histological verification of hypotheses of anatomical correspondence of MR-based parcellations , but also hamper the reconstruction of histological information into 3D space. "
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    ABSTRACT: Ultra-high field magnetic resonance imaging (MRI) became increasingly relevant for in vivo neuroscientific research because of improved spatial resolutions. However, this is still the unchallenged domain of histological studies, which long played an important role in the investigation of neuropsychiatric disorders. While the field of biological psychiatry strongly advanced on macroscopic levels, current developments are rediscovering the richness of immunohistological information when attempting a multi-level systematic approach to brain function and dysfunction. For most studies, histology sections lost information on three-dimensional reconstructions. Translating histological sections to 3D-volumes would thus not only allow for multi-stain and multi-subject alignment in post mortem data, but also provide a crucial step in big data initiatives involving the network analyses currently performed with in vivo MRI. We therefore investigated potential pitfalls during integration of MR and histological information where no additional blockface information is available. We demonstrated that strengths and requirements from both methods can be effectively combined at a spatial resolution of 200 μm. However, the success of this approach is heavily dependent on choices of hardware, sequence and reconstruction. We provide a fully automated pipeline that optimizes histological 3D reconstructions, providing a potentially powerful solution not only for primary human post mortem research institutions in neuropsychiatric research, but also to help alleviate the massive workloads in neuroanatomical atlas initiatives. We further demonstrate (for the first time) the feasibility and quality of ultra-high spatial resolution (150 μm isotopic) imaging of the entire human brain MRI at 7T, offering new opportunities for analyses on MR-derived information.
    Frontiers in Neuroanatomy 09/2013; 7:31. DOI:10.3389/fnana.2013.00031 · 3.54 Impact Factor
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    • "They offer various ways of presenting the data, easy navigation, searching, cross referencing, with possibilities of dynamic managing of the content (e.g. Baldock et al. 2003; Chakravarty et al. 2006; Gustafson et al. 2007; Mikula et al. 2007; Nowinski et al. 2012). Distribution of the atlases and related data, access flexibility through both programmatic and graphical interfaces, are almost as important as the data themselves. "
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    ABSTRACT: Brain atlases are important tools of neuroscience. Traditionally prepared in paper book format, more and more commonly they take digital form which extends their utility. To simplify work with different atlases, to lay the ground for developing universal tools which could abstract from the origin of the atlas, efforts are being made to provide common interfaces to these atlases. 3D Brain Atlas Reconstructor service (3dBARs) described here is a repository of digital representations of different brain atlases in CAF format which we recently proposed and a repository of 3D models of brain structures. A graphical front-end is provided for creating and viewing the reconstructed models as well as the underlying 2D atlas data. An application programming interface (API) facilitates programmatic access to the service contents from other websites. From a typical user’s point of view, 3dBARs offers an accessible way to mine publicly available atlasing data with a convenient browser based interface, without the need to install extra software. For a developer of services related to brain atlases, 3dBARs supplies mechanisms for enhancing functionality of other software. The policy of the service is to accept new datasets as delivered by interested parties and we work with the researchers who obtain original data to make them available to the neuroscience community at large. The functionality offered by the 3dBARs situates it at the core of present and future general atlasing services tying it strongly to the global atlasing neuroinformatics infrastructure. Electronic Supplementary Material The online version of this article (doi:10.1007/s12021-013-9199-9) contains supplementary material, which is available to authorized users.
    Neuroinformatics 08/2013; 11(4). DOI:10.1007/s12021-013-9199-9 · 2.83 Impact Factor
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