A neural network architecture for automatic segmentation of fluorescence micrographs

A system for the automatic segmentation of fluorescence micrographs is presented. In a first step positions of fluorescent cells are detected by a fast learning neural network, which acquires the visual knowledge from a set of training cell-image patches selected by the user. Guided by the detected cell positions the system extracts in the second step the contours of the cells. For contour extraction a recurrent neural network model is used to approximate the cell shapes. Even though the micrographs are noisy and the fluorescent cells vary in shape and size, the system detects at minimum

• Type: Conference
• Year: 2000
• Where: ESANN
• Authors: Tim W. Nattkemper, Heiko Wersing, Walter Schubert, Helge Ritter
• Keywords: ESANN 2000, ESANN 2007, Fluorescent Cells, Learning Neural Network, Neural Network