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.

S3 Net: Trained on a Small Sample Segmentation Network for Biomedical Image Analysis Fully convolutional networks (FCNs) are powerful methods to extract hierarchies of features that have achieved remarkable success in various biomedical image analysis tasks. However, the successful training of FCN requires more than hundreds of pixellevel annotated training samples, which poses a challenge for biomedical image processing tasks. In this paper, we present  S3  Net, a network that makes more efficient use of available annotated samples on biomedical image segmentation.  S3  Net is essentially a deeplysupervised encoderdecoder network where the decoder has been redesigned to efficiently restore multilevel encoded feature resolution in a single step. We have conducted extensive experiments on the 2015 MICCAI Gland Challenge dataset. Compared with other methods,  S3  Net achieves a 0.781 dicescore using 11 images for training, higher than UNet 18.6% and UNet 12.9%, which verifies the performance of  S3  Net trained on a small sample. Further, with training on 85 images,  S3  Net achieves a 0.910 dicescore by using resnet50 as the encoder, which is higher than stateoftheart semantic segmentation results by 4%.