Combining 2D and 3D Features to Classify Protein Mutants in HeLa Cells

The field of high-throughput applications in biomedicine is an always enlarging field. This kind of applications, providing a huge amount of data, requires necessarily semi-automated or fully automated analysis systems. Such systems are typically represented by classifiers capable of discerning from the different types of data obtained (i.e. classes). In this work we present a methodology to improve classification accuracy in the field of 3D confocal microscopy. A set of 3D cellular images (z-stacks) were taken, each depicting HeLa cells with different mutations of the UCE protein ([Mannose-6-Phosphate] UnCovering Enzyme). This dataset was classified to obtain the mutation class from the z-stacks. 3D and 2D features were extracted, and classifications were carried out with cell by cell and z-stack by z-stack approaches, with 2D or 3D features. Also, a classification approach that combines 2D and 3D features is proposed, which showed interesting improvements in the classification accuracy.