Automated Neuron Tracing Methods: An Updated Account

Acciai, Ludovica; Soda, Paolo; Iannello, Giulio

doi:10.1007/s12021-016-9310-0

Cited by 93 publications

(80 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We thus find it interesting to extrapolate recent trends into the foreseeable future. A linear rate of progress would predict the availability of 2.5 million neuron tracings by 2026, though advances in imaging and reconstruction automation 25 could further accelerate the pace of data collection. This figure falls between the number of neurons in the most popular invertebrate and vertebrate animal models (fruit fly; ~10 5 , mouse; ~10 8 ).…”

Section: Peering Around the Next Decadementioning

confidence: 99%

Win–win data sharing in neuroscience

et al. 2017

View full text Add to dashboard Cite

show abstract

Section: Peering Around the Next Decadementioning

confidence: 99%

Win–win data sharing in neuroscience

et al. 2017

View full text Add to dashboard Cite

show abstract

“…or patho-physiology (e.g. the detection of neuronal morphological anomalies in diseased individuals compared to healthy ones) (Acciai et al 2016). In conclusion, the SmRG can facilitate the identification of the different neural types populating the brain, providing an unprecedented set of morphological information and new impetus towards connectomic mapping.…”

Section: Resultsmentioning

confidence: 99%

“…Confocal and 2-photon datasets are characterized by on-plane and intra-plane pixel intensity heterogeneities, deriving from optical phenomena and the non-uniform distribution of fluorophores through the sample (Diaspro 2001). Given these intrinsic features, a valid procedure for accurately digitizing the neural structures in the stack could be obtained by leveraging on local approaches and methods enforcing spatial constraints, such as region growing procedures (RG) (Acciai et al 2016;Brice and Fennema 1970;Xiao and Peng 2013). RG is a pixel intensity-based segmentation method that identifies a Region of Interest (ROI) starting from a pixel, i.e.…”

mentioning

confidence: 99%

Smart Region-Growing: a novel algorithm for the segmentation of 3D clarified confocal image stacks

Magliaro

Ahluwalia

et al. 2018

Preprint

View full text Add to dashboard Cite

Motivation: Accurately mapping the brain at the micro-scale is still a challenge in cellular neuroscience. While notable success has been reached in the field of tissue clarification and confocal imaging to obtain high-fidelity maps of three-dimensional neuron organization, neuron segmentation is still far away of ground-truth and manual segmentation performed by experts may be needed. The need of an expert is in part related to the limited success of the algorithms and tools performing single-neuron segmentation from 3D microscopic image data available in the State of Art, in part to the non-complete information given by these methods, which typically perform neuron tracing and thus limit the interpretability of results. Results: In this paper, a novel algorithm for segmenting single neurons in their own arrangement within the brain is presented. The algorithm performs a region growing procedure with local thresholds based on the pixel intensity statistics typical of confocal acquisitions of biological samples and described by a mixture model. The algorithm is developed and tested on 3D confocal datasets obtained from clarified tissues. We compare the result of our algorithm with those obtained by manual segmentation performed by 6 different experts in terms of neuron surface area, volume and Sholl profiles. Statistical analysis performed on morphologic features extracted from the segmented structures confirms the feasibility of our approach. Availability: The Smart Region Growing (SmRG) algorithm used in the analysis along with test confocal image stacks is available on request to the authors. Contact:

show abstract

“…Meijering [31] noted that data quality was the primary reason these algorithms fail in practice, as the current state-of-the-art methods provide error-free results only in highly optimal conditions. For a full review and comparison of recent methods, we refer readers to a recent paper by Acciai et al [4]. …”

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

Automated Neuron Tracing Methods: An Updated Account

Cited by 93 publications

References 51 publications

Win–win data sharing in neuroscience

Win–win data sharing in neuroscience

Smart Region-Growing: a novel algorithm for the segmentation of 3D clarified confocal image stacks

A Virtual Reality Visualization Tool for Neuron Tracing

Contact Info

Product

Resources

About