Multi-class Cell Segmentation Using CNNs with F\(_1\)-measure Loss Function

Cell segmentation is one of the fundamental problems in biomedical image processing as it is often mandatory for the quantitative analysis of biological processes. Sometimes, a binary segmentation of the cells is not sufficient, for instance if biologists are interested in the appearance of specific cell parts. Such a setting requires multiple foreground classes, which can significantly increase the complexity of the segmentation task. This is especially the case if very fine structures need to be detected. Here, we propose a method for multi-class segmentation of Drosophila macrophages in in-vivo fluorescence microscopy images to segment complex cell structures such as the lamellipodium and filopodia. Our approach is based on a convolutional neural network, more specifically the U-net architecture. The network is trained using a loss function based on the F\(_1\)-measure which we have extended for multi-class scenarios to account for class imbalances in the image data. We compare the F\(_1\)-measure loss function to a weighted cross entropy loss and show that the CNN outperforms other segmentation approaches.