MultiClass Segmentation of Lung Immunofluorescence Confocal Images Using Deep Learning Deep learning models are now widely applied to various biomedical image analysis tasks such as the image segmentation and classification. However, automation of biomedical image analysis with deep learning is challenging since it requires highly specialized knowledge and large amounts of training data. In this work, we detail automatic multiclass segmentations using deep learning models for lung immunofluorescent (IF) confocal images, along with synthetic image generation of lung images for training these models. Analysis of lung imaging data is important for understanding the lung development at the molecular level and crosssectional IF images are useful in identifying various structures of the lung. We tested multiclass segmentation using deep learning convolutional neural network (CNN) models with overwrap cropping method as preprocessing to make the dataset larger. Further, we generated synthetic images using deep convolution generative synthetic adversarial network (DCGAN) and use them in learned segmentation networks for creating segmentation masks. In terms of deep learning segmentation models, we adapted the stateoftheart UNet, SegNet, and DeepLabv3 based models for multiclass segmentation from lung IF images. Our experimental results on these challenging lung IF images show that the highest dice score for training 98.7%, and testing 87.0% is obtained by an adapted multiclass UNet method. Further, our synthetic image generation shows promise for future training paradigms in improving the segmentation of various lung structures in IF confocal images.

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.