A segmentation method combining probability map and boundary based on multiple fully convolution network and repetitive training.

A segmentation method combining probability map and boundary based on multiple fully convolution network and repetitive training. Phys Med Biol. 2019 Feb 26;: Authors: Yin W, Hu Y, Yi S, He J Abstract Cell nuclei image segmentation technology can help researchers observe each cell's stress response to drug treatment. However, it is still a challenge to accurately segment the adherent cell nuclei. At present, image segmentation based on fully convolutional network (FCN) is attracting researcher's attention. We propose a Multiple FCN architecture and Repetitive Training (M-FCN-RT) method to learn features of cell nucleus images. In M-FCN-RT, the Multiple FCN (M-FCN) architecture is composed of several Single FCNs (S-FCN) with the same structure, and each FCN is used to learn the specific features of image datasets. In this paper, M-FCN contains three FCNs with FCN1-2, FCN3 and FCNB. FCN1-2 and FCN3 are respectively used to learn the spatial features of cell nuclei for generating probability maps to indicate nucleus regions of an image; another FCNB (Boundary-FCN) is used to learn the edge features of cell nuclei for generating nucleus boundary. For each FCN training, we propose a Repetitive Training (RT) method to improve the classification accuracy of the model. To segment cell nuclei, we finally propose an algorithm combing the Probability Map and Boundary (PMB) to segment the adherent nuclei. This paper uses a public opening nucleus...
Source: Physics in Medicine and Biology - Category: Physics Authors: Tags: Phys Med Biol Source Type: research