Abstract
Automatic detection and segmentation of neurons from microscopy acquisition is essential for statistically characterizing neuron morphology that can be related to their functional role. In this paper, we propose a combined pipeline that starts from the automatic detection of the soma through a new multiscale blob enhancement filtering. Then, a precise segmentation of the detected cell body is obtained by an active contour approach. The resulted segmentation is used as initial seed for the second part of the approach that proposes a dendrite arborization tracing method.
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References
Baden, T., Berens, P., Franke, K., Rosón, M.R., Bethge, M., Euler, T.: The functional diversity of retinal ganglion cells in the mouse. Nature 529(7586), 345–350 (2016)
Meijering, E.: Neuron tracing in perspective. Cytom. Part A 77(7), 693–704 (2010)
Basu, S., Aksel, A., Condron, B., Acton, S.T.: Tree2Tree: neuron segmentation for generation of neuronal morphology. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 548–551. IEEE (2010)
Longair, M.H., Baker, D.A., Armstrong, J.D.: Simple neurite tracer: open source software for reconstruction, visualization and analysis of neuronal processes. Bioinformatics 27(17), 2453–2454 (2011)
Zheng, Z., Hong, P.: Incorporate deep-transfer-learning into automatic 3D neuron tracing. In: The First International Conference on Neuroscience and Cognitive Brain Information, BRAININFO 2016 (2016)
Chan, T.F., Vese, L., et al.: Active contours without edges. IEEE Trans. Image Process. 10, 266–277 (2001)
Yezzi, A., Tsai, A., Willsky, A.: A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 13(1), 195–216 (2002)
Ge, Q., Li, C., Shao, W., Li, H.: A hybrid active contour model with structured feature for image segmentation. Signal Process. 108, 147–158 (2015)
Wu, P., Yi, J., Zhao, G., Huang, Z., Qiu, B., Gao, D.: Active contour-based cell segmentation during freezing and its application in cryopreservation. IEEE Trans. Biomed. Eng. 62(1), 284–295 (2015)
Lee, T.C., Kashyap, R.L., Chu, C.N.: Building skeleton models via 3-D medial surface axis thinning algorithms. CVGIP: Graph. Models Image Process. 56(6), 462–478 (1994)
Palágyi, K., Kuba, A.: A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognit. Lett. 19(7), 613–627 (1998)
Meijering, E.H., Jacob, M., Sarria, J.C.F., Unser, M.: A novel approach to neurite tracing in fluorescence microscopy images. In: SIP, pp. 491–495 (2003)
Benmansour, F., Cohen, L.D.: Tubular structure segmentation based on minimal path method and anisotropic enhancement. Int. J. Comput. Vis. 92(2), 192–210 (2011)
Türetken, E., González, G., Blum, C., Fua, P.: Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors. Neuroinformatics 9(2–3), 279–302 (2011)
Baglietto, S., Kepiro, I.E., Hilgen, G., Sernagor, E., Murino, V., Sona, D.: Segmentation of retinal ganglion cells from fluorescent microscopy imaging. In: BIOSTEC, pp. 17–23 (2017)
Gulyanon, S., Sharifai, N., Kim, M.D., Chiba, A., Tsechpenakis, G.: CRF formulation of active contour population for efficient three-dimensional neurite tracing. In: 2016 IEEE 13th International Symposium on Biomedical Imaging, ISBI, pp. 593–597. IEEE (2016)
Lankton, S., Tannenbaum, A.: Localizing region-based active contours. IEEE Trans. Image Process. 17(11), 2029–2039 (2008)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056195
Liu, J., White, J.M., Summers, R.M.: Automated detection of blob structures by Hessian analysis and object scale. In: 2010 17th IEEE International Conference on Image Processing, ICIP, pp. 841–844. IEEE (2010)
Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: International Workshop on Image Processing, Real-Time Edge and Motion Detection (1979)
Lathen, G., Jonasson, J., Borga, M.: Phase based level set segmentation of blood vessels. In: 19th International Conference on Pattern Recognition, ICPR 2008, pp. 1–4. IEEE (2008)
Läthén, G., Jonasson, J., Borga, M.: Blood vessel segmentation using multi-scale quadrature filtering. Pattern Recognit. Lett. 31(8), 762–767 (2010)
Zijdenbos, A.P., Dawant, B.M., Margolin, R., Palmer, A.C., et al.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imag. 13(4), 716–724 (1994)
Mukherjee, S., Condron, B., Acton, S.T.: Tubularity flow field—A technique for automatic neuron segmentation. IEEE Trans. Image Process. 24(1), 374–389 (2015)
Acknowledgements
The research received financial support from the \(7^{th}\) Framework Programme for Research of the European Commision, Grant agreement no. 600847: RENVISION project of the Future and Emerging Technologies (FET) programme.
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Baglietto, S., Kepiro, I.E., Hilgen, G., Sernagor, E., Murino, V., Sona, D. (2018). Automatic Segmentation of Neurons from Fluorescent Microscopy Imaging. In: Peixoto, N., Silveira, M., Ali, H., Maciel, C., van den Broek, E. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2017. Communications in Computer and Information Science, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-319-94806-5_7
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