Abstract
The recent availability of serial block face scanning electron microscopy has permitted researchers to reconstruct cells and neurons by manually identifying and coloring objects. This technique was instrumental in work such as uncovering the anatomical basis for direction selectivity of vision [1]. Unfortunately, reconstruction involves an expenditure of time which can be expensive or prohibitive. We have developed the Computer Assisted Segmentation Tool (CAST), which produces results that appear similar to manual segmentation with reduced personnel time requirements. Results are shown for serial block face electron micrograph (SBEM) images of Mus musculus retinal axons; however, CAST is capable of operation on other image types. CAST is available under an open source license in a modified version of the TrakEM2 plugin for the popular Fiji image analysis suite. Usage and installation instructions can be found at http://isoptera.lcsc.edu/segmentation_tool/.
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Acknowledgments
We would like to thank the students of the Fall 2016 CS492 Bioinformatics class at Lewis-Clark State College for providing the manual outlines referenced in this study. This research was supported by the INBRE program, NIH Grant No. P20 GM103408 (National Institute of General Medical Sciences).
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Tropea, A.N. et al. (2017). Computer Assisted Segmentation Tool: A Machine Learning Based Image Segmenting Tool for TrakEM2. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_22
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DOI: https://doi.org/10.1007/978-3-319-59575-7_22
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