A method of analyzing growth of individuated microbial clones comprising: introducing a sample comprising microbial cells to a microfluidic device comprising a surface with a detection surface; capturing the microbial cells on the detection surface; introducing growth media with or without an antimicrobial agent to the captured microbial cells; acquiring a first and a second image of said sample separated by a period of time; acquiring optical image data from said first image and said second image of the individuated microbial cells over time; seeding said optical image data by undertaking at least one of smoothing, denoising, and background estimation and segmentation, thereby generating a list of microbial cell coordinates identified as potential seed points; registering the microbial cell coordinates by computing a step-wise registration shift in a time-lapse series, wherein the registration shift is two numbers which represent the translation in pixels that is required for the first image and the second image to be aligned; object model fitting data around a seed point to identify which portions of optical image change between the first image and the second image as time progresses thereby identifying individuated microbial cells, wherein microbial cell objects are characterized by one or more of length and width in signal parameters, orientation angle in the plane on a signal, height in signal intensity units, and microbial cell signal intensity comprising a composite value of a plurality of measured attributes; tracking the individuated microbial cells by spectral cluster analysis using an iteratively calculated distance measure (σ dist) 2 of a Gaussian similarity function, representing the distance of new cells to parent cells between the first image and the second image in the time-lapse series to identify microbial cells belonging to the same clone, wherein the distance measure (σ dist) 2 is increased until all microbial cells of the second image are associated with a clone in the first image; and analyzing via a probability analysis pixel intensity data for each clone to obtain a growth likelihood value. introducing a sample comprising microbial cells to a microfluidic device comprising a surface with a detection surface; capturing the microbial cells on the detection surface; introducing growth media with or without an antimicrobial agent to the captured microbial cells; acquiring a first and a second image of said sample separated by a period of time; acquiring optical image data from said first image and said second image of the individuated microbial cells over time; seeding said optical image data by undertaking at least one of smoothing, denoising, and background estimation and segmentation, thereby generating a list of microbial cell coordinates identified as potential seed points; registering the microbial cell coordinates by computing a step-wise registration shift in a time-lapse series, wherein the registration shift is two numbers which represent the translation in pixels that is required for the first image and the second image to be aligned; object model fitting data around a seed point to identify which portions of optical image change between the first image and the second image as time progresses thereby identifying individuated microbial cells, wherein microbial cell objects are characterized by one or more of length and width in signal parameters, orientation angle in the plane on a signal, height in signal intensity units, and microbial cell signal intensity comprising a composite value of a plurality of measured attributes; tracking the individuated microbial cells by spectral cluster analysis using an iteratively calculated distance measure (σ dist) 2 of a Gaussian similarity function, representing the distance of new cells to parent cells between the first image and the second image in the time-lapse series to identify microbial cells belonging to the same clone, wherein the distance measure (σ dist) 2 is increased until all microbial cells of the second image are associated with a clone in the first image; and analyzing via a probability analysis pixel intensity data for each clone to obtain a growth likelihood value.