A computer-implemented method of training a model that includes a set of rules for classifying pixels associated with cellular objects (202, 302, 402) in an image (200, 300, 400), comprising: receiving an image (200, 300, 400); and receiving training data, wherein the training data comprises a list of pixel coordinates and an object classification with which such coordinate is associated, such that the training data identifies a pixel associated with an object (202, 302, 402) of a particular type in the image, and wherein the particular type is a biological cell (202, 302, 402); characterized by developing a plurality of filtered versions of the image (200, 300, 400) by applying one or more image filters of a filtering module to the image (200, 300, 400); processing the training data and the plurality of filtered versions of the image (200, 300, 400) to develop a first set of rules for classifying pixels associated with objects of the particular type, wherein applying the first set of rules identifies pixels associated with boundaries between confluent biological cells in the image (200, 300, 400); applying the first set of rules to the image (200, 300, 400) to identify pixels associated with a plurality of objects of the particular type in the image (200, 300, 400); evaluating the effectiveness of the first set of rules by applying each rule against the training data and determining the percent of pixels in the training data that are correctly identified using such rules, thereby identifying a second set of rules that are able to correctly classify at least a predetermined percentage of pixels in the image, wherein the second set of rules is stored as a trained model and the remaining rules are discarded, determining the filtered images that are used by the second set of rules for classifying pixels of the training data and adjusting the set of filters by the filtering module accordingly, and updating parameters for the filtering module, wherein the filters that will be applied by the filtering module to subsequent images are adjusted so that the filtering module applies to subsequent acquired images only those filters that are associated with the filtered images used by the second set of rules so that the unnecessary filters are not applied; and imaging processing steps to refine the object segmentation. receiving an image (200, 300, 400); and receiving training data, wherein the training data comprises a list of pixel coordinates and an object classification with which such coordinate is associated, such that the training data identifies a pixel associated with an object (202, 302, 402) of a particular type in the image, and wherein the particular type is a biological cell (202, 302, 402); characterized by developing a plurality of filtered versions of the image (200, 300, 400) by applying one or more image filters of a filtering module to the image (200, 300, 400); processing the training data and the plurality of filtered versions of the image (200, 300, 400) to develop a first set of rules for classifying pixels associated with objects of the particular type, wherein applying the first set of rules identifies pixels associated with boundaries between confluent biological cells in the image (200, 300, 400); applying the first set of rules to the image (200, 300, 400) to identify pixels associated with a plurality of objects of the particular type in the image (200, 300, 400); evaluating the effectiveness of the first set of rules by applying each rule against the training data and determining the percent of pixels in the training data that are correctly identified using such rules, thereby identifying a second set of rules that are able to correctly classify at least a predetermined percentage of pixels in the image, wherein the second set of rules is stored as a trained model and the remaining rules are discarded, determining the filtered images that are used by the second set of rules for classifying pixels of the training data and adjusting the set of filters by the filtering module accordingly, and updating parameters for the filtering module, wherein the filters that will be applied by the filtering module to subsequent images are adjusted so that the filtering module applies to subsequent acquired images only those filters that are associated with the filtered images used by the second set of rules so that the unnecessary filters are not applied; and imaging processing steps to refine the object segmentation.