Deep convolutional neural networks (dCNN) for image segmentation, instance labeling, and tracking.

Project Description Our research group has been using deep convolutional neural networks (CNNs) to segment out biological structures from both time lapse confocal microscopy data sets and three dimensional electron microscopy. We have developed a pipeline that encompasses every step between image acquisition on microscopes, deep learning-based denoising and segmentation, visualization, and image analysis. Last summer, we successfully trained an undergraduate on our pipeline to segment mitochondria from 3D electron microscopy datasets. In this project, we are seeking a student that would like to learn how to implement this pipeline, and in the process, develop new capabilities for our pipeline. The data we use comes from our Bio-Imaging Center that serves over 100 research groups each year. The goal is to develop a flexible deep learning pipeline that can be readily deployed for a wide range of research projects. In this example project, we will examine cross sections of anthers, which produce pollen, that have a distinctive radial organization of tissue layers. The same sample will be imaged by both super-resolution light microscopy and electron microscopy. Images will be overlaid and aligned and both can be used for deep learning. Some hand traced training data of cell outlines has already been generated, making rapid progress possible. In the first month, the student would learn how to use this limited data set to train a CNN and then predict segmentation on new images. Then, the student would manually fix errors in these new predictions to increase the size of the training data set. It is expected that this process will take an additional month to complete. Once cells are adequately segmented, the remainder of the time would be to take that knowledge and use a CNN to classify different cell types and tissue layers. All of this work will be done using the Biomix cluster at the Delaware Biotechnology Institute.