3D Deep Convolutional Neural Networks in Lattice LightSheet Data Puncta Segmentation Recent advances in fluorescence imaging technology such as adaptive optics lattice lightsheet microscopy (AOLLSM) allow live imaging of three dimensional (3D) tissues at superresolution in 4D (xyzt) at seconds frame rates 1. The resulting datasets contain novel biology but are higher dimensional and much larger (100s of GB  3D movie) than datasets of traditional (e.g. confocal) microscopy. New tools and methods are necessary to segment and quantify these datasets such as the library pyLattice 2. Here we present pyLattice_deepLearning, an image segmentation module based on 3D deep convolutional neural networks (3D UNets 3). Using virtual machines on the Google Cloud Platform, the segmentation of a xe2x88xbc180MB 3D frame takes xe2x88xbc30s, which is xe2x88xbc7x faster than standard pyLattice v1.0. pyLattice_deepLearning is particularly well suited for very low signaltonoise situations where retrieval of xe2x88xbc85% of the objects in a noisy image is possible while standard pyLattice retrieves xe2x88xbc17%.

A TwoTier Convolutional Neural Network for Combined Detection and Segmentation in Biological Imagery Deep learning techniques have been useful for modern microscopy imaging techniques to further study and analyze biological structures and organs. Convolutional neural networks (CNN) have improved 2D object detection, localization, and segmentation. For imagery containing biological structures with depth, it is especially desirable to perform these tasks in 3D. Traditionally, performing these tasks simultaneously in 3D has proven to be computationally expensive. Currently available methodologies thus largely work to segment 3D objects from 2D images (without context from captured 3D volumes). In this work, we present a novel approach to perform fast and accurate localization, detection, and segmentation of volumes containing cells. Specifically, in our method, we modify and tune two stateoftheart CNNs, namely 2D YOLOv2 and 3D UNet, and combine them with a new fusion and image processing algorithms. Annotated volumes in this space are limited, and we have created synthetic data that mimics actual structures for training and testing our proposed approach. Promising results on this test data demonstrate the value of the technique and offers a methodology for 3D cell analysis in real microscopy imagery.