An octree-based multiresolution approach supporting interactive rendering of very large volume data sets

Figure 1: Local re nement of nested ROIs in foot data set. Abstract We present an octree-based approach supporting multiresolution volume rendering of large data sets. Given a set of scattered points without connectivity information, we impose an octree data structure of low resolution in the preprocessing step. The construction of this initial octree structure is controlled by the original data resolution and cell-speci c error values. Using the octree nodes, rather than the data points, as elementary units for ray casting, we rst generate a crude rendering of a given data set. Keeping the pre-processing step independent from the rendering step, we allow a user to interactively explore a large data set by specifying a region of interest (ROI), where a higher level of rendering accuracy Center of Image Processing and Integrated Computing is desired. To re ne an ROI, we are making use of the octree constructed in the pre-processing step. Our approach is aimed at minimizing the number of computations and can be applied to large-scale data exploration tasks

Network-based Rendering Techniques for Large-scale Volume Data Sets

Large biomedical volumetric data sets are usually stored as file sets, where the files represent a family of cross sections. Interactive rendering of large data sets requires fast access to user-defined parts of the data, because it is virtually impossible to render an entire data set of such an enormous size (several gigabytes) at full resolution, and to transfer such data upon request over the Internet in a reasonable amount of time. Therefore, hierarchical rendering techniques have been introduced to render a region of interest at a relatively higher resolution. Regions rendered at coarser resolutions are provided as context information. We present a dynamic subdivision scheme that incorporates space-subdivision and wavelet compression

3D Mapping of Serial Histology Sections with Anomalies Using a Novel Robust Deformable Registration Algorithm

The neuroimaging field is moving toward micron scale and molecular features in digital pathology and animal models. These require mapping to common coordinates for annotation, statistical analysis, and collaboration. An important example, the BRAIN Initiative Cell Census Network, is generating 3D brain cell atlases in mouse, and ultimately primate and human. We aim to establish RNAseq profiles from single neurons and nuclei across the mouse brain, mapped to Allen Common Coordinate Framework (CCF). Imaging includes (Forumala Presented). 500 tape-transfer cut 20 (Forumala Presented). m thick Nissl-stained slices per brain. In key areas 100 $$\upmu $$ m thick slices with 0.5–2 mm diameter circular regions punched out for snRNAseq are imaged. These contain abnormalities including contrast changes and missing tissue, two challenges not jointly addressed in diffeomorphic image registration. Existing methods for mapping 3D images to histology require manual steps unacceptable for high throughput, or are sensitive to damaged tissue. Our approach jointly: registers 3D CCF to 2D slices, models contrast changes, estimates abnormality locations. Our registration uses 4 unknown deformations: 3D diffeomorphism, 3D affine, 2D diffeomorphism per-slice, 2D rigid per-slice. Contrast changes are modeled using unknown cubic polynomials per-slice. Abnormalities are estimated using Gaussian mixture modeling. The Expectation Maximization algorithm is used iteratively, with E step: compute posterior probabilities of abnormality, M step: registration and intensity transformation minimizing posterior-weighted sum-of-square-error. We produce per-slice anatomical labels using Allen Institute’s ontology, and publicly distribute results online, with several typical and abnormal slices shown here. This work has further applications in digital pathology, and 3D brain mapping with stroke, multiple sclerosis, or other abnormalities