Deep Neural Network Based Cell Segmentation for Lab-on-CMOS Systems using Realtime Microscopy

Several deep neural network-based image processing techniques were compared for the analysis of cellular behavior of cells cultured directly on Lab-on-CMOS devices. Lab-on- CMOS devices are typically opaque and use integrated circuits to implement functionality, so they must be observed using reflection mode microscopy and have prominent background features. These factors significantly increase the difficulty of the cell segmentation task due to image distortion and complex backgrounds. In this paper, we describe several techniques that have been implemented for use in the characterization of a Lab-on-CMOS capacitance sensor. Relative to previously reported approaches based on morphological filtering, the neural-net based approaches reported here improve the intersection-over-union metric for image segmentation from 57% to 85%. These tools will enable insight into the capabilities of capacitance sensing modalities for the monitoring of single cell events.