Automated scalable segmentation of neurons from multispectral images
Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.
The authors proposed a methdo to automatically segment neurons from multispectral images. The algorithm is based on a 5 steps pipeline which makes us of standard techniques from image processing (voxelization, watershed) and from machine learning (spectral clustering) in order to reconstruct the neurons shape.
After reading the author's rebuttal I have increased the technical quality to 2 and after reading the the other reviews I increased the potential impact to 3.  The authors replied to many questions but not to all, in particular the answer was not satisfactory to the question about the parameter K which is one of the crucial parameter in any segmentation algorithm. Why they did not provide the results using the suggested automatic method in Fig4 instead of cyclying on possible (wrong) number of clusters ? this will asses the generality of the method. I would have expected to see in the results the performances with at least one auto-tuning heuristic to asses its generality (at least the one suggested by the authors).  --------  The paper, despite being written nicely, suffer of some major lacks that makes it not acceptable for NIPS.   In the following the issues found in the paper:  1) In Eq(2) when constructing the adjecency matrix, the ranges of the distances d(...) and \delta(...) are the same ? otherwise is not possible to merge them into the same function otherwise undesired effects in the final affinity may occur.   2) In line 125, d(u,v) is the same as the one in Eq(2) and line 114 ? In the line 114 d(s) is a measure of heterogeneity, in line 125 of  distance and in Eq(2) of color distance.  3) Is not clear why the authors used clustering method that requires an input parameter K when other clustering algorithm exists and that do not require such parameter (i.e. dbscan, affinity propagation)  4) Since the pipeline uses the spectral clusterig algorithm, why the authors did not use the Spectral Gap heuristic to determine the K ?  5) In which sense the parameter K (line 143) has been manually estimated ? did you run the algorithm several time and use the K that produces the better results ?  6) Is not written which function has been used to compute d(s) (line 114). Authors just wrote an example of a possible function to be used but not the actual one.  7) In line 114, d(s) is the same as the one used in Eq(2) ?  8) Is completely arbitrary the number of "reliable" and "unreliable" neighbors because is not explained why the authors decided to use 0.5 and 0.05 (line 120 and 122).  9) The two threshold t_s and t_d how has been set ? is not written in the text.  10) In line 132, I suppose, that the equation e^-{\alpha d^2_{ij}}... \times (0.1 + 0.9 ...) is referred to the Eq.(2). If one expand that equation it results in 0.1e^{-\alpha d_{ij} +0.9e^-{\alpha d_{ij}}e^{\beta \delta{ij}} which is actually not consistent with Eq(2). Please explain.  Qualitatively the performances (in particular in Fig 3) looks very nice but there are too many details that are not clear.  Grammatically speaking, the paper is written nicely withour major nor minor typos.  In the following the motivations for the scores:  Technical quality 1: there are too many unclear things and math formulations that are ambiguous or wrong  Novelty/originality 2: because the work combine known methods from image processing and machine learning  Potential impact or usefulness 2: this work can have an important impact in the society to understand the shape of neurons but due to the issues in the methodological and technical parts in am not sure that it can be applied on different dataset than the one used in this paper. It seems to be higly customized.  Clarity and presentation 2: because the usage of the math notation is not correct and ambiguous.