Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and Applications

Bone and mineral researc

Three-Dimensional In Vivo Imaging of the Murine Liver: A Micro-Computed Tomography-Based Anatomical Study

Various murine models are currently used to study acute and chronic pathological processes of the liver, and the efficacy of novel therapeutic regimens. The increasing availability of high-resolution small animal imaging modalities presents researchers with the opportunity to precisely identify and describe pathological processes of the liver. To meet the demands, the objective of this study was to provide a three-dimensional illustration of the macroscopic anatomical location of the murine liver lobes and hepatic vessels using small animal imaging modalities. We analysed micro-CT images of the murine liver by integrating additional information from the published literature to develop comprehensive illustrations of the macroscopic anatomical features of the murine liver and hepatic vasculature. As a result, we provide updated three-dimensional illustrations of the macroscopic anatomy of the murine liver and hepatic vessels using micro-CT. The information presented here provides researchers working in the field of experimental liver disease with a comprehensive, easily accessable overview of the macroscopic anatomy of the murine liver

The Digital Fish Library: Using MRI to Digitize, Database, and Document the Morphological Diversity of Fish

Museum fish collections possess a wealth of anatomical and morphological data that are essential for documenting and understanding biodiversity. Obtaining access to specimens for research, however, is not always practical and frequently conflicts with the need to maintain the physical integrity of specimens and the collection as a whole. Non-invasive three-dimensional (3D) digital imaging therefore serves a critical role in facilitating the digitization of these specimens for anatomical and morphological analysis as well as facilitating an efficient method for online storage and sharing of this imaging data. Here we describe the development of the Digital Fish Library (DFL, http://www.digitalfishlibrary.org), an online digital archive of high-resolution, high-contrast, magnetic resonance imaging (MRI) scans of the soft tissue anatomy of an array of fishes preserved in the Marine Vertebrate Collection of Scripps Institution of Oceanography. We have imaged and uploaded MRI data for over 300 marine and freshwater species, developed a data archival and retrieval system with a web-based image analysis and visualization tool, and integrated these into the public DFL website to disseminate data and associated metadata freely over the web. We show that MRI is a rapid and powerful method for accurately depicting the in-situ soft-tissue anatomy of preserved fishes in sufficient detail for large-scale comparative digital morphology. However these 3D volumetric data require a sophisticated computational and archival infrastructure in order to be broadly accessible to researchers and educators

Segmentation of Skull in 3D Human MR Images Using Mathematical Morphology

We present a new technique for segmentation of skull in human T1-weighted magnetic resonance (MR) images that generates realistic models of the head for EEG and MEG source modeling. Our method performs skull segmentation using a sequence of mathematical morphological operations. Prior to the segmentation of skull, we segment the scalp and the brain from the MR image. The scalp mask allows us to quickly exclude background voxels with intensities similar to those of the skull, while the brain mask obtained from our Brain Surface Extractor algorithm ensures that the brain does not intersect our skull segmentation. We find the inner and the outer skull boundaries using thresholding and morphological closing and opening operations. We then mask the results with the scalp and brain volumes to ensure closed and nonintersecting skull boundaries. We applied our scalp and skull segmentation algorithm to several MR images and validated our method using coregistered CT-MR image data sets. We observe that our method is capable of producing scalp and skull segmentations suitable for MEG and EEG source modeling in 3D T1-weighted human MR images