How physics-informed neural networks improve image segmentation performance

Many quantitative cell biology experiments depend on rapid and faithful segmentation (the computational identification) of cells in microscopy images. Recent developments in deep learning have greatly improved these algorithms; however, significant challenges remain, especially in the context of cells with unusual morphologies. A critical limitation of deep learning is the size and diversity of the training set, which often must be hand segmented by an expert. An alternative approach is the use of data augmentation, using a physics-informed neural network, to supplement training. We demonstrate that this approach greatly increases the performance of the segmentation algorithm. This is a promising example of a potentially widely applicable approach: the use of physics to constrain a deep learning model to increase performance at fixed training set size.