Confocal microscopy allows visualization of biopolymer networks at the nano scale. Analyzing the structure and assembly of protein networks from images requires a segmentation process. This has proven to be challenging due to multiple possible sources of noise in images as well as exhibition of out-of-focus planes. Here, we present a deep learning-based segmentation procedure for confocal laser scanning microscopy images of biopolymer networks. Utilizing an encoder-decoder network architecture, our deep neural network achieved a dice score of 0.88 in segmenting images of filamentous temperature sensitive Z proteins from chloroplasts ofPhyscomitrella patens, a moss.