Dense-UNet: a novel multiphoton in vivo cellular image segmentation model based on a convolutional neural network

Abstract

The deepened down-sampling path improved the ability of the model to capture cellular fined-detailed boundary features, while the symmetrical up-sampling path provided a more accurate location based on the test result. These results were the first time that the segmentation of MPM <i>in vivo</i> images had been adopted by introducing a deep CNN to bridge this gap in Dense-UNet technology. Dense-UNet has reached ultramodern performance for MPM images, especially for <i>in vivo</i> images with low resolution. This implementation supplies an automatic segmentation model based on deep learning for high-precision segmentation of MPM images <i>in vivo</i>.