Cancer cell detection and invasion depth estimation in brightfield images

Abstract

The study of cancer cell invasion under the effect of different conditions is fundamental for the understanding of the cancer invasion mechanism and to test possible therapies for its regulation. To simulate invasion across tissue basement membrane, biologists established in vitro assays with cancer cells invading extracellular matrix components. However, analysis of such assays is manual, being timeconsuming and error-prone, which motivates an objective and automated analysis tool. Towards automating such analysis we present a methodology to detect cells in 3D matrix cell assays and correctly estimate their invasion, measured by the depth of the penetration in the gel. Detection is based on the sliding band filter, by evaluating the gradient convergence and not intensity. As such it can detect low contrast cells which otherwise would be lost. For cell depth estimation we present a focus estimator based on the convergence gradients magnitude. The final cell detections precision and recall are of 0.896 and 0.910 respectively, and the average error in the cells position estimate is of 0.41µm, 0.37µm and 3.7µm in the x, y and z directions, respectively.