DeepXScope: Segmenting Microscopy Images with a Deep Neural Network

• Main Authors: Saponaro, Philip, Treible, Wayne, Kolagunda, Abhishek, Chaya, Timothy, Caplan, Jeffrey, Kambhamettu, Chandra, Wisser, Randall
• Format: Conference Proceeding
• Language: English
• Subjects: Computer architecture; Image segmentation; Measurement; Microscopy; Three-dimensional displays; Training; Training data
• ISSN: 2160-7516
• DOI: 10.1109/CVPRW.2017.117

High-speed confocal microscopy has shown great promise to yield insights into plant-fungal interactions by allowing for large volumes of leaf tissue to be imaged at high magnification. Currently, segmentation is performed either manually, which is infeasible for large amounts of data, or by developing separate algorithms to extract individual features within the image data. In this work, we propose the use of a single deep convolutional neural network architecture dubbed DeepXScope for automatically segmenting hyphal networks of the fungal pathogen and cell boundaries and stomata of the host plant. DeepXScope is trained on manually annotated images created for each of these structures. We describe experiments that show each individual structure can be accurately extracted automatically using DeepXScope. We anticipate that plant scientists will be able to use this network to automatically extract multiple structures of interest, and we plan to release our tool to the community 1 .