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Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections.

Papp EA, Leergaard TB, Csucs G, Bjaalie JG - Front Neuroinform (2016)

Bottom Line: Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling.For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection.Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates.

View Article: PubMed Central - PubMed

Affiliation: Institute of Basic Medical Sciences, University of Oslo Oslo, Norway.

ABSTRACT
Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.

No MeSH data available.


Related in: MedlinePlus

Results of automatic labeling detection in images of microscopic sections. Coronal sections from corresponding anteroposterior levels through the pontine nuclei (PN) and the cerebral peduncle (ped) with BDA labeling and Neutral Red counterstain (A), and Pha-L labeling with thionine counterstain (B). Processed images highlighting detected labeling are shown below the original sections (A’,B’). Dense clusters of labeled fibers are well preserved in the labeling maps. Note that noise levels are higher in the BDA image compared to the Pha-L image. Scale bar: 1 mm
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Figure 3: Results of automatic labeling detection in images of microscopic sections. Coronal sections from corresponding anteroposterior levels through the pontine nuclei (PN) and the cerebral peduncle (ped) with BDA labeling and Neutral Red counterstain (A), and Pha-L labeling with thionine counterstain (B). Processed images highlighting detected labeling are shown below the original sections (A’,B’). Dense clusters of labeled fibers are well preserved in the labeling maps. Note that noise levels are higher in the BDA image compared to the Pha-L image. Scale bar: 1 mm

Mentions: The aim of the image processing, referred to as automatic labeling detection, was to detect labeled axons or cell bodies (referred to as signal), and discard other entities including non-labeled tissue elements and diffuse non-specific background (noise). We tested the image processing workflow module on several series of images with varying densities of BDA and Pha-L labeled axons, with and without counterstaining for cytoarchitecture (Neutral Red or thionine). We then compared the binary maps generated by the automatic labeling method with the distribution of labeling as observed in the original section images, and with results obtained in a previously published study using the same series of images but a rigorous manual mapping approach (Zakiewicz et al., 2014). Labeling is divided into three categories, in agreement with Zakiewicz et al. (2014): (1) High amounts of labeling are defined as dense clusters of labeled fibers located closely together so that individual cells or fibers cannot be discerned. High amounts of labeling are typically found in locations with terminal fields of fibers or at tracer injection sites (Figure 3). (2) Modest amounts of labeling feature relatively fewer fibers, separated but not readily counted. (3) Low amounts of labeling comprise a countable number of single fibers. The comparison of the binary maps with the results of the manual analysis, taking into consideration the three categories (high, modest, and low), allowed us to evaluate the automatic labeling detection method with regard to the amount of undetected labeling (false negatives), the detection limit for labeling, the level of noise, and the consistency of the signal-to-noise ratio throughout large series of images.


Brain-Wide Mapping of Axonal Connections: Workflow for Automated Detection and Spatial Analysis of Labeling in Microscopic Sections.

Papp EA, Leergaard TB, Csucs G, Bjaalie JG - Front Neuroinform (2016)

Results of automatic labeling detection in images of microscopic sections. Coronal sections from corresponding anteroposterior levels through the pontine nuclei (PN) and the cerebral peduncle (ped) with BDA labeling and Neutral Red counterstain (A), and Pha-L labeling with thionine counterstain (B). Processed images highlighting detected labeling are shown below the original sections (A’,B’). Dense clusters of labeled fibers are well preserved in the labeling maps. Note that noise levels are higher in the BDA image compared to the Pha-L image. Scale bar: 1 mm
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4835481&req=5

Figure 3: Results of automatic labeling detection in images of microscopic sections. Coronal sections from corresponding anteroposterior levels through the pontine nuclei (PN) and the cerebral peduncle (ped) with BDA labeling and Neutral Red counterstain (A), and Pha-L labeling with thionine counterstain (B). Processed images highlighting detected labeling are shown below the original sections (A’,B’). Dense clusters of labeled fibers are well preserved in the labeling maps. Note that noise levels are higher in the BDA image compared to the Pha-L image. Scale bar: 1 mm
Mentions: The aim of the image processing, referred to as automatic labeling detection, was to detect labeled axons or cell bodies (referred to as signal), and discard other entities including non-labeled tissue elements and diffuse non-specific background (noise). We tested the image processing workflow module on several series of images with varying densities of BDA and Pha-L labeled axons, with and without counterstaining for cytoarchitecture (Neutral Red or thionine). We then compared the binary maps generated by the automatic labeling method with the distribution of labeling as observed in the original section images, and with results obtained in a previously published study using the same series of images but a rigorous manual mapping approach (Zakiewicz et al., 2014). Labeling is divided into three categories, in agreement with Zakiewicz et al. (2014): (1) High amounts of labeling are defined as dense clusters of labeled fibers located closely together so that individual cells or fibers cannot be discerned. High amounts of labeling are typically found in locations with terminal fields of fibers or at tracer injection sites (Figure 3). (2) Modest amounts of labeling feature relatively fewer fibers, separated but not readily counted. (3) Low amounts of labeling comprise a countable number of single fibers. The comparison of the binary maps with the results of the manual analysis, taking into consideration the three categories (high, modest, and low), allowed us to evaluate the automatic labeling detection method with regard to the amount of undetected labeling (false negatives), the detection limit for labeling, the level of noise, and the consistency of the signal-to-noise ratio throughout large series of images.

Bottom Line: Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling.For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection.Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates.

View Article: PubMed Central - PubMed

Affiliation: Institute of Basic Medical Sciences, University of Oslo Oslo, Norway.

ABSTRACT
Axonal tracing techniques are powerful tools for exploring the structural organization of neuronal connections. Tracers such as biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin (Pha-L) allow brain-wide mapping of connections through analysis of large series of histological section images. We present a workflow for efficient collection and analysis of tract-tracing datasets with a focus on newly developed modules for image processing and assignment of anatomical location to tracing data. New functionality includes automatic detection of neuronal labeling in large image series, alignment of images to a volumetric brain atlas, and analytical tools for measuring the position and extent of labeling. To evaluate the workflow, we used high-resolution microscopic images from axonal tracing experiments in which different parts of the rat primary somatosensory cortex had been injected with BDA or Pha-L. Parameters from a set of representative images were used to automate detection of labeling in image series covering the entire brain, resulting in binary maps of the distribution of labeling. For high to medium labeling densities, automatic detection was found to provide reliable results when compared to manual analysis, whereas weak labeling required manual curation for optimal detection. To identify brain regions corresponding to labeled areas, section images were aligned to the Waxholm Space (WHS) atlas of the Sprague Dawley rat brain (v2) by custom-angle slicing of the MRI template to match individual sections. Based on the alignment, WHS coordinates were obtained for labeled elements and transformed to stereotaxic coordinates. The new workflow modules increase the efficiency and reliability of labeling detection in large series of images from histological sections, and enable anchoring to anatomical atlases for further spatial analysis and comparison with other data.

No MeSH data available.


Related in: MedlinePlus