A neural network architecture for automatic segmentation of fluorescence micrographs

被引：29
|
作者：
Nattkemper, TW [1 ]
Wersing, H
Schubert, W
Ritter, H
机构：
[1] Univ Bielefeld, Neuroinformat Grp, D-4800 Bielefeld, Germany
[2] Univ Magdeburg, Inst Med Neurobiol, Neuroimmunol & Mol Pattern Recognit Grp, D-39106 Magdeburg, Germany
[3] Univ Magdeburg, MELTEC Ltd, D-39106 Magdeburg, Germany
来源：
NEUROCOMPUTING | 2002年 / 48卷
关键词：
segmentation; contour grouping; fluorescence microscopy; functional proteomics;
D O I：
10.1016/S0925-2312(01)00642-7
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A system for the automatic segmentation of fluorescence micrographs is presented. In the 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 95% of the cells. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：357 / 367
页数：11
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