The DIADEM and Beyond

Liu, Yuan

doi:10.1007/s12021-011-9102-5

Cited by 47 publications

(36 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Together with whole-brain-scale data acquisition, structural reconstruction is a grand challenge for connectomics. The DIADEM (Digital Reconstruction of Axonal and Dendritic Morphology) Challenge and lessons learned from the first round of competition show a long road ahead of us in terms of accurate circuit tracing (Liu, 2011). The KESM data sets are basically image stacks and they do not provide quantitative morphological or connectivity data.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Chung

Sung

Mayerich

et al. 2011

Front. Neuroinform.

View full text Add to dashboard Cite

Connectomics is the study of the full connection matrix of the brain. Recent advances in high-throughput, high-resolution 3D microscopy methods have enabled the imaging of whole small animal brains at a sub-micrometer resolution, potentially opening the road to full-blown connectomics research. One of the first such instruments to achieve whole-brain-scale imaging at sub-micrometer resolution is the Knife-Edge Scanning Microscope (KESM). KESM whole-brain data sets now include Golgi (neuronal circuits), Nissl (soma distribution), and India ink (vascular networks). KESM data can contribute greatly to connectomics research, since they fill the gap between lower resolution, large volume imaging methods (such as diffusion MRI) and higher resolution, small volume methods (e.g., serial sectioning electron microscopy). Furthermore, KESM data are by their nature multiscale, ranging from the subcellular to the whole organ scale. Due to this, visualization alone is a huge challenge, before we even start worrying about quantitative connectivity analysis. To solve this issue, we developed a web-based neuroinformatics framework for efficient visualization and analysis of the multiscale KESM data sets. In this paper, we will first provide an overview of KESM, then discuss in detail the KESM data sets and the web-based neuroinformatics framework, which is called the KESM brain atlas (KESMBA). Finally, we will discuss the relevance of the KESMBA to connectomics research, and identify challenges and future directions.

show abstract

Section: Discussionmentioning

confidence: 99%

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Chung

Sung

Mayerich

et al. 2011

Front. Neuroinform.

View full text Add to dashboard Cite

show abstract

“…Automated methods for neuron reconstruction continue to improve; however, neuroscientists often find the results of these algorithms unsatisfactory [26]. Thus, tracing neurons remains primarily a manual task.…”

Section: Background and Related Workmentioning

confidence: 99%

A Virtual Reality Visualization Tool for Neuron Tracing

Usher

Klacansky

Federer

et al. 2018

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

Tracing neurons in large-scale microscopy data is crucial to establishing a wiring diagram of the brain, which is needed to understand how neural circuits in the brain process information and generate behavior. Automatic techniques often fail for large and complex datasets, and connectomics researchers may spend weeks or months manually tracing neurons using 2D image stacks. We present a design study of a new virtual reality (VR) system, developed in collaboration with trained neuroanatomists, to trace neurons in microscope scans of the visual cortex of primates. We hypothesize that using consumer-grade VR technology to interact with neurons directly in 3D will help neuroscientists better resolve complex cases and enable them to trace neurons faster and with less physical and mental strain. We discuss both the design process and technical challenges in developing an interactive system to navigate and manipulate terabyte-sized image volumes in VR. Using a number of different datasets, we demonstrate that, compared to widely used commercial software, consumer-grade VR presents a promising alternative for scientists.

show abstract

“…In the latter case, an unprecedented resolution can be achieved by first clarifying the tissue (Hama et al, 2011; Chung et al, 2013), and then imaging the entire brain from thousands of optical sections (Ragan et al, 2012). The overwhelming obstacle remaining on the way to brain mapping is accurate, high-throughput tracing of neurons (Sporns et al, 2005; Lichtman et al, 2008; Miller, 2010; Gillette et al, 2011b; Kozloski, 2011; Lichtman and Denk, 2011; Liu, 2011; Svoboda, 2011; Helmstaedter and Mitra, 2012; Van Essen and Ugurbil, 2012; Perkel, 2013). Presently, accurate traces of complex neuron morphologies can only be obtained manually, which is extremely time consuming (Stepanyants et al, 2004, 2008; Shepherd et al, 2005), and thus impractical for large reconstruction projects.…”

Section: Introductionmentioning

confidence: 99%

Active learning of neuron morphology for accurate automated tracing of neurites

et al. 2014

View full text Add to dashboard Cite

Automating the process of neurite tracing from light microscopy stacks of images is essential for large-scale or high-throughput quantitative studies of neural circuits. While the general layout of labeled neurites can be captured by many automated tracing algorithms, it is often not possible to differentiate reliably between the processes belonging to different cells. The reason is that some neurites in the stack may appear broken due to imperfect labeling, while others may appear fused due to the limited resolution of optical microscopy. Trained neuroanatomists routinely resolve such topological ambiguities during manual tracing tasks by combining information about distances between branches, branch orientations, intensities, calibers, tortuosities, colors, as well as the presence of spines or boutons. Likewise, to evaluate different topological scenarios automatically, we developed a machine learning approach that combines many of the above mentioned features. A specifically designed confidence measure was used to actively train the algorithm during user-assisted tracing procedure. Active learning significantly reduces the training time and makes it possible to obtain less than 1% generalization error rates by providing few training examples. To evaluate the overall performance of the algorithm a number of image stacks were reconstructed automatically, as well as manually by several trained users, making it possible to compare the automated traces to the baseline inter-user variability. Several geometrical and topological features of the traces were selected for the comparisons. These features include the total trace length, the total numbers of branch and terminal points, the affinity of corresponding traces, and the distances between corresponding branch and terminal points. Our results show that when the density of labeled neurites is sufficiently low, automated traces are not significantly different from manual reconstructions obtained by trained users.

show abstract

The DIADEM and Beyond

Cited by 47 publications

References 6 publications

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

A Virtual Reality Visualization Tool for Neuron Tracing

Active learning of neuron morphology for accurate automated tracing of neurites

Contact Info

Product

Resources

About