MCUnet: Multiscale Convolution Unet for Bladder Cancer Cell Segmentation in PhaseContrast Microscopy Images Owing to the high density, low contrast, deformable cell shapes, low intercellular shape and appearance variation, and occlusion of the cells by division or fusion especially in phasecontrast microscopy images, it is still a challenging task to segment cells from the complex background. In this work, we proposed a multiscale convolution Unet (MCUnet) for bladder cancer cell segmentation in PhaseContrast microscopy images. More specifically, the second 3x3 convolution of each layer in the standard Unet is replaced with a multiscale convolution (MC) block with different kernel sizes, such as 1x1, 3x3, and 5x5. To verify the effectiveness of the proposed method, a series of experiments are conducted on the bladder cancer T24 dataset and the MoNuSeg dataset, and the results shows the proposed MCUnet can obtain better comprehensive performance than the standard Unet.

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.