Papers:

Cell-X (Zurich):  http://www.csb.ethz.ch/tools/software/cellx.html
    - Approach written in MATLAB, for use in cell segmentation
    - Uses CLAHE to threshold, then uses unique membrane dynamics to examine cell boundaries.
        - https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btu302

Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images:  https://academic.oup.com/bioinformatics/article/30/17/i587/201138/Large-scale-automated-identification-of-mouse
    - Approach:
        - Substack entire thing into WxHxD pieces
        - Make sure substacks overlap (some dimension M) such as to prevent borders from improperly being trained (make sure all cells fall within WxHxD?  need more reading)
        - Two inputs: L, S (each point intensity represented as (x, y, z))
            - L - set of voxels whose intensities exceed background intensity
            - S - specially selected subset to run mean-shift algorithm
        - Mean shift algorithm
            - The classic mean shift algorithm would start from all available data points, place a kernel on each of them and shift each point toward the mean value computed as the kernel-weighted average of the data.
            - In this variant, they improve both its running time and its statistical precision by starting from a carefully chosen set of seeds S
        - Thresholding
            - Found background/saturated areas and removed as basic filters.
        - Supervised semantic deconvolution
            - Above step works well on bright images.  Darker somas might be missed.
            - To improve over this intrinsic difficulty, they carried out a preprocessing stage by applying a non-linear filter trained to boost weak somata and decrease the voxel intensities in non-soma regions.

Gradient Flow:  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4578315/
    - Approach is to find "gradient" of background (ie - derivatives w.r.t X, Y, Z) and set them to zero