The Chemical Imaging Initiative at the Pacific Northwest National Laboratory (PNNL) is creating a `Rapid Experimental Analysis' (REXAN) Framework, based on the concept of reusable component libraries. REXAN allows developers to quickly compose and customize high throughput analysis pipelines for a range of experiments, as well as supporting the creation of multi-modal analysis pipelines. In addition, PNNL has coupled REXAN with its collaborative data management and analysis environment Velo to create an easy to use data management and analysis environments for experimental facilities. This paper will discuss the benefits of Velo and REXAN in the context of three examples: PNNL High Resolution Mass Spectrometry - reducing analysis times from hours to seconds, and enabling the analysis of much larger data samples (100KB to 40GB) at the same time. · ALS X-Ray Tomography - reducing analysis times of combined STXM and EM data collected at the ALS from weeks to minutes, decreasing manual work and increasing data volumes that can be analysed in a single step. · Multi-modal nano-scale analysis of STXM and TEM data - providing a semi automated process for particle detection. The creation of REXAN has significantly shortened the development time for these analysis pipelines. The integration of Velo and REXAN has significantly increased the scientific productivity of the instruments and their users by creating easy to use data management and analysis environments with greatly reduced analysis times and improved analysis capabilities.

Analysis of gene expression patterns in brain images obtained from high-throughput in situ hybridization requires accurate and consistent annotations of anatomical regions/subregions. Such annotations are obtained by mapping an anatomical atlas onto the gene expression images through intensity- and/or landmark-based registration methods or deformable model-based segmentation methods. Due to the complex appearance of the gene expression images, these approaches require a pre-processing step to determine landmark correspondences in order to incorporate landmark-based geometric constraints. In this paper, we propose a novel method for landmark-constrained, intensity-based registration without determining landmark correspondences a priori. The proposed method performs dense image registration and identifies the landmark correspondences, simultaneously, using a single higher-order Markov Random Field model. In addition, a machine learning technique is used to improve the discriminating properties of local descriptors for landmark matching by projecting them in a Hamming space of lower dimension. We qualitatively show that our method achieves promising results and also compares well, quantitatively, with the expert's annotations, outperforming previous methods.

An accurate labeling of a multi-part, complex anatomical structure (e.g., brain) is required in order to compare data across images for spatial analysis. It can be achieved by fitting an object-specific geometric atlas that is constructed using a partitioned, high-resolution deformable mesh and tagging each of its polygons with a region label. Subdivision meshes have been used to construct such an atlas because they can provide a compact representation of a partitioned, multi-resolution, object-specific mesh structure using only a few control points. However, automated fitting of a subdivision mesh-based geometric atlas to an anatomical structure in an image is a difficult problem and has not been sufficiently addressed. In this paper, we propose a novel Markov Random Field-based method for fitting a planar, multi-part subdivision mesh to anatomical data. The optimal fitting of the atlas is obtained by determining the optimal locations of the control points. We also tackle the problem of landmark matching in tandem with atlas fitting by constructing a single graphical model to impose pose-invariant, landmark-based geometric constraints on atlas deformation. The atlas deformation is also governed by additional constraints imposed by the mesh's geometric properties and the object boundary. We demonstrate the potential of the proposed method on the difficult problem of segmenting a mouse brain and its interior regions in gene expression images which exhibit large intensity and shape variability. We obtain promising results when compared with manual annotations and prior methods.

Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the more than 20 000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, is being used to create a searchable database of gene expression patterns in the adult mouse brain. Our framework annotates the images about four times faster and has achieved a median spatial overlap of up to 0.92 compared with expert segmentation in 64 images tested. This tool is intended to help scientists interpret large-scale gene expression patterns more efficiently.

Massive amounts of data are being generated in an effort to represent for the brain the expression of all genes at cellular resolution. Critical to exploiting this effort is the ability to place these data into a common frame of reference. Here we have developed a computational method for annotating gene expression patterns in the context of a digital atlas to facilitate custom user queries and comparisons of this type of data. This procedure has been applied to 200 genes in the postnatal mouse brain. As an illustration of utility, we identify candidate genes that may be related to Parkinson disease by using the expression of a dopamine transporter in the substantia nigra as a search query pattern. In addition, we discover that transcription factor Rorb is down-regulated in the barrelless mutant relative to control mice by quantitative comparison of expression patterns in layer IV somatosensory cortex. The semi-automated annotation method developed here is applicable to a broad spectrum of complex tissues and data modalities.

As the logical next step after sequencing the mouse genome, biologists have developed laboratory methods for rapidly determining where each of the 30K genes in the mouse genome is synthesizing protein. Applying these methods to the mouse brain, biologists are currently generating large numbers of 2D cross-sectional images that record the expression pattern for each gene in the mouse genome. In this paper, we describe the structure of a geometric database for the mouse brain that allows biologists to organize and search this gene expression data. The central component of this database is an atlas that explicitly partitions the mouse brain into key anatomical regions. This atlas is represented as a Catmull-Clark subdivision mesh with anatomical regions separated by a network of B-spline crease curves. New gene expression images are added to the database by deforming this atlas onto each image using techniques developed for fitting subdivision surfaces to scatter data. Due to this partitioning of the subdivision mesh, user queries comparing expression data between various genes can be restricted to anatomical regions without difficulty while the multi-resolution structure of the subdivision mesh allows these queries to be processed efficiently.