BranchAnalysis2D/3D automates morphometry analyses of branching structures
•An open-source algorithm for analysis of branching structures is presented.•Algorithm output matches output from human observers using existing analysis tools.•The algorithm is faster than human observers using other analysis tools.•BranchAnalysis2D/3D automation decreases investigator bias.•Branch...
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| Veröffentlicht in: | Journal of neuroscience methods Jg. 294; S. 1 - 6 |
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15.01.2018
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| Abstract | •An open-source algorithm for analysis of branching structures is presented.•Algorithm output matches output from human observers using existing analysis tools.•The algorithm is faster than human observers using other analysis tools.•BranchAnalysis2D/3D automation decreases investigator bias.•BranchAnalysis2D/3D can be used to measure any branching structure.
Morphometric analyses of biological features have become increasingly common in recent years with such analyses being subject to a large degree of observer bias, variability, and time consumption. While commercial software packages exist to perform these analyses, they are expensive, require extensive user training, and are usually dependent on the observer tracing the morphology.
To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin we call ‘BranchAnalysis2D/3D’, to perform morphometric analyses of structures with branching morphologies, such as neuronal dendritic spines, vascular morphology, and primary cilia.
Our BranchAnalysis2D/3D algorithm allows for rapid quantification of the length and thickness of branching morphologies, independent of user tracing, in both 2D and 3D data sets.
We validated the performance of BranchAnalysis2D/3D against pre-existing software packages using trained human observers and images from brain and retina. We found that the BranchAnalysis2D/3D algorithm outputs results similar to available software (i.e., Metamorph, AngioTool, Neurolucida), while allowing faster analysis times and unbiased quantification.
BranchAnalysis2D/3D allows inexperienced observers to output results like a trained observer but more efficiently, thereby increasing the consistency, speed, and reliability of morphometric analyses. |
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| AbstractList | Morphometric analyses of biological features have become increasingly common in recent years with such analyses being subject to a large degree of observer bias, variability, and time consumption. While commercial software packages exist to perform these analyses, they are expensive, require extensive user training, and are usually dependent on the observer tracing the morphology.BACKGROUNDMorphometric analyses of biological features have become increasingly common in recent years with such analyses being subject to a large degree of observer bias, variability, and time consumption. While commercial software packages exist to perform these analyses, they are expensive, require extensive user training, and are usually dependent on the observer tracing the morphology.To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin we call 'BranchAnalysis2D/3D', to perform morphometric analyses of structures with branching morphologies, such as neuronal dendritic spines, vascular morphology, and primary cilia.NEW METHODTo address these issues, we have developed a broadly applicable, no-cost ImageJ plugin we call 'BranchAnalysis2D/3D', to perform morphometric analyses of structures with branching morphologies, such as neuronal dendritic spines, vascular morphology, and primary cilia.Our BranchAnalysis2D/3D algorithm allows for rapid quantification of the length and thickness of branching morphologies, independent of user tracing, in both 2D and 3D data sets.RESULTSOur BranchAnalysis2D/3D algorithm allows for rapid quantification of the length and thickness of branching morphologies, independent of user tracing, in both 2D and 3D data sets.We validated the performance of BranchAnalysis2D/3D against pre-existing software packages using trained human observers and images from brain and retina. We found that the BranchAnalysis2D/3D algorithm outputs results similar to available software (i.e., Metamorph, AngioTool, Neurolucida), while allowing faster analysis times and unbiased quantification.COMPARISON WITH EXISTING METHODSWe validated the performance of BranchAnalysis2D/3D against pre-existing software packages using trained human observers and images from brain and retina. We found that the BranchAnalysis2D/3D algorithm outputs results similar to available software (i.e., Metamorph, AngioTool, Neurolucida), while allowing faster analysis times and unbiased quantification.BranchAnalysis2D/3D allows inexperienced observers to output results like a trained observer but more efficiently, thereby increasing the consistency, speed, and reliability of morphometric analyses.CONCLUSIONSBranchAnalysis2D/3D allows inexperienced observers to output results like a trained observer but more efficiently, thereby increasing the consistency, speed, and reliability of morphometric analyses. •An open-source algorithm for analysis of branching structures is presented.•Algorithm output matches output from human observers using existing analysis tools.•The algorithm is faster than human observers using other analysis tools.•BranchAnalysis2D/3D automation decreases investigator bias.•BranchAnalysis2D/3D can be used to measure any branching structure. Morphometric analyses of biological features have become increasingly common in recent years with such analyses being subject to a large degree of observer bias, variability, and time consumption. While commercial software packages exist to perform these analyses, they are expensive, require extensive user training, and are usually dependent on the observer tracing the morphology. To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin we call ‘BranchAnalysis2D/3D’, to perform morphometric analyses of structures with branching morphologies, such as neuronal dendritic spines, vascular morphology, and primary cilia. Our BranchAnalysis2D/3D algorithm allows for rapid quantification of the length and thickness of branching morphologies, independent of user tracing, in both 2D and 3D data sets. We validated the performance of BranchAnalysis2D/3D against pre-existing software packages using trained human observers and images from brain and retina. We found that the BranchAnalysis2D/3D algorithm outputs results similar to available software (i.e., Metamorph, AngioTool, Neurolucida), while allowing faster analysis times and unbiased quantification. BranchAnalysis2D/3D allows inexperienced observers to output results like a trained observer but more efficiently, thereby increasing the consistency, speed, and reliability of morphometric analyses. Morphometric analyses of biological features have become increasingly common in recent years with such analyses being subject to a large degree of observer bias, variability, and time consumption. While commercial software packages exist to perform these analyses, they are expensive, require extensive user training, and are usually dependent on the observer tracing the morphology. To address these issues, we have developed a broadly applicable, no-cost ImageJ plugin we call 'BranchAnalysis2D/3D', to perform morphometric analyses of structures with branching morphologies, such as neuronal dendritic spines, vascular morphology, and primary cilia. Our BranchAnalysis2D/3D algorithm allows for rapid quantification of the length and thickness of branching morphologies, independent of user tracing, in both 2D and 3D data sets. We validated the performance of BranchAnalysis2D/3D against pre-existing software packages using trained human observers and images from brain and retina. We found that the BranchAnalysis2D/3D algorithm outputs results similar to available software (i.e., Metamorph, AngioTool, Neurolucida), while allowing faster analysis times and unbiased quantification. BranchAnalysis2D/3D allows inexperienced observers to output results like a trained observer but more efficiently, thereby increasing the consistency, speed, and reliability of morphometric analyses. |
| Author | Srinivasan, Aditya Sheen, Volney L. Pumiglia, Kevin M. Ferland, Russell J. Muñoz-Estrada, Jesús Bourgeois, Justin R. Nalwalk, Julia W. |
| AuthorAffiliation | 2 Department of Regenerative Cell and Cancer Cell Biology, Albany Medical College, Albany, NY 12208 3 Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115 1 Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208 4 Department of Neurology, Albany Medical College, Albany, NY 12208 |
| AuthorAffiliation_xml | – name: 3 Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115 – name: 1 Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208 – name: 2 Department of Regenerative Cell and Cancer Cell Biology, Albany Medical College, Albany, NY 12208 – name: 4 Department of Neurology, Albany Medical College, Albany, NY 12208 |
| Author_xml | – sequence: 1 givenname: Aditya surname: Srinivasan fullname: Srinivasan, Aditya email: sriniva1@amc.edu organization: Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, United States – sequence: 2 givenname: Jesús surname: Muñoz-Estrada fullname: Muñoz-Estrada, Jesús organization: Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, United States – sequence: 3 givenname: Justin R. surname: Bourgeois fullname: Bourgeois, Justin R. organization: Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, United States – sequence: 4 givenname: Julia W. surname: Nalwalk fullname: Nalwalk, Julia W. organization: Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, United States – sequence: 5 givenname: Kevin M. surname: Pumiglia fullname: Pumiglia, Kevin M. organization: Department of Regenerative Cell and Cancer Cell Biology, Albany Medical College, Albany, NY 12208, United States – sequence: 6 givenname: Volney L. surname: Sheen fullname: Sheen, Volney L. organization: Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115, United States – sequence: 7 givenname: Russell J. surname: Ferland fullname: Ferland, Russell J. email: ferlanr@amc.edu organization: Department of Neuroscience and Experimental Therapeutics, Albany Medical College, Albany, NY 12208, United States |
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| Keywords | Branching morphology Morphometry Primary cilia Spines Vasculature |
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| SubjectTerms | Algorithms Animals Brain - cytology Branching morphology Imaging, Three-Dimensional - methods Mice Microscopy, Confocal - methods Morphometry Neurons - cytology Observer Variation Primary cilia Reproducibility of Results Retina - anatomy & histology Software Spines Vasculature |
| Title | BranchAnalysis2D/3D automates morphometry analyses of branching structures |
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