Changes in cell nuclear architecture are regulated by complex biological mechanisms that associated with the altered functional properties of a cell. Quantitative analyses of structural alterations of nuclei and their compartments are important for understanding such mechanisms. In this work we present a comparison of approaches for nuclear structure classification, evaluated on 2D per-channel representations from a large 3D microscopy imaging dataset by maximum intensity projection. Specifically, we compare direct classification of pixel data from either raw intensity images or binary masks that contain only information about morphology of the object, but not texture. We evaluate a number of widely used classification algorithms using 2 different cross-validation schemes to assess batch effects. We compare obtained results with the previously reported baselines and discuss novel findings.