Abstract
To date, automated or semi-automated software and algorithms for segmentation of neurons from three-dimensional imaging datasets have had limited success. The gold standard for neural segmentation is considered to be the manual isolation performed by an expert. To facilitate the manual isolation of complex objects from image stacks, such as neurons in their native arrangement within the brain, a new Manual Segmentation Tool (ManSegTool) has been developed. ManSegTool allows user to load an image stack, scroll down the images and to manually draw the structures of interest stack-by-stack. Users can eliminate unwanted regions or split structures (i.e., branches from different neurons that are too close each other, but, to the experienced eye, clearly belong to a unique cell), to view the object in 3D and save the results obtained. The tool can be used for testing the performance of a single-neuron segmentation algorithm or to extract complex objects, where the available automated methods still fail. Here we describe the software's main features and then show an example of how ManSegTool can be used to segment neuron images acquired using a confocal microscope. In particular, expert neuroscientists were asked to segment different neurons from which morphometric variables were subsequently extracted as a benchmark for precision. In addition, a literature-defined index for evaluating the goodness of segmentation was used as a benchmark for accuracy. Neocortical layer axons from a DIADEM challenge dataset were also segmented with ManSegTool and compared with the manual “gold-standard” generated for the competition.
Highlights
Understanding how the brain works is arguably one of the greatest challenges of our time (Alivisatos et al, 2012)
Both the three-dimensional coordinates extracted from the gold standard ∗.swc and those extracted from the segmented neuron are plotted
The reconstruction and the study of neuronal morphology from three-dimensional image stacks is considered a crucial task in neuro-scientific research, as it could help elucidate the relationship between structure and function in the brain
Summary
Understanding how the brain works is arguably one of the greatest challenges of our time (Alivisatos et al, 2012). To understand the structure-function relationship in the brain, the first step is to identify the 3D (three-dimensional) arrangement of a single cell in its native environment within the brain from neuroimaging data This key task could enable studying the morphological properties of neurons, to investigate the factors influencing neural development and alterations related to specific diseases (Iannicola et al, 2000; Solis et al, 2007; Billeci et al, 2013), the relationships between neuronal shape and function (Costa Lda et al, 2002; Brown et al, 2005; White, 2007) or the effects of specific compounds on neuron geometry. Confocal and multi-photon microscopy have revolutionized neurobiological discoveries by allowing the study of micro-structure (Ntziachristos, 2010) Even these methods cannot image intact brain samples more than few hundred micrometers in thickness (Oheim et al, 2001). The recently developed clarification methods render the brain optically transparent (Regehr et al, 2009; Hama et al, 2011; Chung and Deisseroth, 2013; Chung et al, 2013; Kuwajima et al, 2013; Ertürk et al, 2014; Poguzhelskaya et al, 2014; Richardson and Lichtman, 2015; Magliaro et al, 2016), allowing a notable increase of light penetration depth, which enables the visualization of the global arrangement of large brain cell populations
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