NOISE AND ARTIFACT REMOVAL IN KNIFE-EDGE SCANNING MICROSCOPY

Knife-Edge Scanning Microscopy (KESM) is a recently de-veloped technique that allows fast and automated imaging of several hundred cubic millimeters of tissue at sub-micron res-olution. Successive sections are captured in registration by imaging the specimen concurrently with cutting by a diamond-knife ultramicrotome. Because this imaging technique is rel-atively new, we are currently investigating ways to improve image quality and data rate. In addition, certain imaging ar-tifacts are unique to this technology and the time required to perform corrective image processing is a concern due to the high rate of image capture. In this paper, we describe algo-rithms that can be used to process KESM images in order to obtain the quality necessary for subsequent segmentation and modeling. There is also emphasis on making these algo-rithms independent of global information within the image so that they can be more easily parallelized. Index Terms — knife-edge microscopy, chatter, lighting artifacts, volumetric data capture 1

NetMets: software for quantifying and visualizing errors in biological network segmentation

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization

Serial two-photon tomography for automated ex vivo mouse brain imaging

Here we describe an automated method, named serial two-photon (STP) tomography, that achieves high-throughput fluorescence imaging of mouse brains by integrating two-photon microscopy and tissue sectioning. STP tomography generates high-resolution datasets that are free of distortions and can be readily warped in three dimensions, for example, for comparing multiple anatomical tracings. This method opens the door to routine systematic studies of neuroanatomy in mouse models of human brain disorders

Automated lateral sectioning for knife-edge scanning microscopy

Recent advances in high-throughput microscopy are used to acquire large-scale anatomical information at the microscopic level. One of these methods, known as Knife-Edge Scanning Microscopy (KESM), allows large volumes of tissue to be imaged using physical sectioning. This method has been limited, however, by constraints on the field of view of the objective and the need to prevent damage to tissue before it is imaged. In this paper, we describe a simple sectioning algorithm we use to overcome these constraints on tissue size. By maintaining a height field of the tissue surface, we are able to cut lateral sections while minimizing damage to un-imaged tissue. Although lateral sectioning introduces some deformation and tissue damage at the interface of the sections, the damage is minimal and the deformations can be compensated for using affine transformations. Index Terms — knife-edge scanning microscopy, KESM, microscopy, serial sectioning 1