Micro-Net: A unified model for segmentation of various objects in microscopy images
This work addresses object segmentation for microscopy image analysis, but it appears incremental as it builds on existing CNN methods with specific architectural tweaks.
The authors tackled the problem of segmenting various objects in microscopy images by proposing a unified CNN architecture that trains at multiple resolutions and uses multi-resolution deconvolution, showing it outperforms recent deep learning algorithms on public datasets.
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.