  Object segmentation and structure localization are important steps in
automated image analysis pipelines for microscopy images. We present a
convolution neural network (CNN) based deep learning architecture for
segmentation of objects in microscopy images. The proposed network can be used
to segment cells, nuclei and glands in fluorescence microscopy and histology
images after slight tuning of input parameters. The network trains at multiple
resolutions of the input image, connects the intermediate layers for better
localization and context and generates the output using multi-resolution
deconvolution filters. The extra convolutional layers which bypass the
max-pooling operation allow the network to train for variable input intensities
and object size and make it robust to noisy data. We compare our results on
publicly available data sets and show that the proposed network outperforms
recent deep learning algorithms.
