|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|6267529||1614595||2016||10 صفحه PDF||سفارش دهید||دانلود رایگان|
- New method for soma detection and extraction in 2D and 3D fluorescent images of neurons is proposed.
- Main novelty is the use of anisotropic multiscale filters and Directional Ratio to separate somas from neurites.
- It performs very reliably, accurately and rapidly with respect to existing methods.
- Code is released free and open source.
BackgroundAutomated detection and segmentation of somas in fluorescent images of neurons is a major goal in quantitative studies of neuronal networks, including applications of high-content-screenings where it is required to quantify multiple morphological properties of neurons. Despite recent advances in image processing targeted to neurobiological applications, existing algorithms of soma detection are often unreliable, especially when processing fluorescence image stacks of neuronal cultures.New methodIn this paper, we introduce an innovative algorithm for the detection and extraction of somas in fluorescent images of networks of cultured neurons where somas and other structures exist in the same fluorescent channel. Our method relies on a new geometrical descriptor called Directional Ratio and a collection of multiscale orientable filters to quantify the level of local isotropy in an image. To optimize the application of this approach, we introduce a new construction of multiscale anisotropic filters that is implemented by separable convolution.ResultsExtensive numerical experiments using 2D and 3D confocal images show that our automated algorithm reliably detects somas, accurately segments them, and separates contiguous ones.Comparison with existing methodsWe include a detailed comparison with state-of-the-art existing methods to demonstrate that our algorithm is extremely competitive in terms of accuracy, reliability and computational efficiency.ConclusionsOur algorithm will facilitate the development of automated platforms for high content neuron image processing. A Matlab code is released open-source and freely available to the scientific community.
Journal: Journal of Neuroscience Methods - Volume 274, 1 December 2016, Pages 61-70