A novel framework to integrate convolutional neural network with compressed sensing for cell detection
The ability to detect certain types of cells in a microscopy image is important for a wide range of clinical applications. Cells often present huge variations in density and appearance, and often occupy only a small portion of an image. Consequently, general object detection methods in computer vision do not meet accuracy requirements: false / missed detections prevail. In this paper, we apply convolutional neural network (CNN) to regress a fixed length vector from a microscopy image. Then, L1 minimization / compressed sensing (CS) recovers a variable number of cell locations from this fixed-length predicted vector. Our contribution in this work is combining CS with CNN to solve cell detection problem in a regression framework, which needs to handle a variable number of cells. Our method relies on the observation that the number of pixels indicating cell centroid locations is a tiny fraction of the total image size. Thus, we utilize this sparsity property by CS. The proposed method is evaluated with several state-of-the-art approaches on public cell datasets and it obtains superior or comparable performances.