Characterizing Robustness and Sensitivity of Convolutional Neural Networks in Segmentation of Fluorescence Microscopy Images

Abstract

Convolutional neural networks (CNNs) recently have achieved remarkable success in segmentation of biological fluorescence microscopy images. Because many of these networks were developed initially for general computer vison tasks such as object detection and object recognition, it is necessary to characterize their performance to determine how they meet the needs of related biological studies. So far, performance characterization of such networks has focused primarily on segmentation accuracy. It remains unclear how different networks compare in their robustness in handling images of different conditions and their sensitivity in detecting subtle geometrical changes of biological structures. Here, we develop a method that uses realistic synthetic images to characterize the robustness and sensitivity of such networks. We use the method to compare the performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U-Net, in segmentation of complex morphology of mitochondria. We also compare them against an adaptive active-mask algorithm in performance. We find that both networks outperform the adaptive active-mask algorithm in robustness and sensitivity and that U-Net outperforms FCN Overall, our study provides new insights into the performance of CNNs in segmentation of fluorescence microscopy images.