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
相关论文
共 50 条
  • [41] The effect of convolutional neural network architectures on phase segmentation of composite material X-ray micrographs
    Galvez-Hernandez, Pedro
    Kratz, James
    [J]. JOURNAL OF COMPOSITE MATERIALS, 2023, 57 (18) : 2899 - 2918
  • [42] Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico
    Faculty of Science, Agriculture & Engineering , Newcastle University, Newcastle upon Tyne
    NE1 7RU, United Kingdom
    不详
    138683, Singapore
    不详
    NE1 8ST, United Kingdom
    [J]. Sensors, 19
  • [43] An automatic particle pickup method using a neural network applicable to low-contrast electron micrographs
    Ogura, T
    Sato, C
    [J]. JOURNAL OF STRUCTURAL BIOLOGY, 2001, 136 (03) : 227 - 238
  • [44] An Automatic Segmentation Technique for Color Images based on SOFM Neural Network
    Zhang, Jun
    Hu, Jinglu
    [J]. IJCNN: 2009 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1- 6, 2009, : 1005 - 1010
  • [45] An adaptive neural network system for automatic image segmentation and edge detection
    Nandedkar, AV
    Kondabathula, S
    Rathod, AK
    [J]. Proceedings of the Sixth IASTED International Conference on Signal and Image Processing, 2004, : 630 - 636
  • [46] Automatic Segmentation Method of Garment Figure Based on Convolutional Neural Network
    Chen, Chong
    [J]. PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELING AND SIMULATION (AMMS 2017), 2017, 153 : 249 - 252
  • [47] Automatic breast segmentation in digital mammography using a convolutional neural network
    Maghsoudi, Omid Haji
    Gastounioti, Aimilia
    Pantalone, Lauren
    Conant, Emily
    Kontos, Despina
    [J]. 15TH INTERNATIONAL WORKSHOP ON BREAST IMAGING (IWBI2020), 2020, 11513
  • [48] An Automatic Image Segmentation Algorithm Based on Spiking Neural Network Model
    Lin, Xianghong
    Wang, Xiangwen
    Cui, Wenbo
    [J]. INTELLIGENT COMPUTING THEORY, 2014, 8588 : 248 - 258
  • [49] Automatic Parameter Setting of Pulse Coupled Neural Network for Image Segmentation
    Dharwal, Devanshu
    Shanker, Ravi
    Bhattacharya, Mahua
    [J]. 2016 INTERNATIONAL CONFERENCE ON COMMUNICATION AND SIGNAL PROCESSING (ICCSP), VOL. 1, 2016, : 2157 - 2161
  • [50] Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation
    Sathananthavathi .V
    Indumathi .G
    Swetha Ranjani .A
    [J]. Journal of Digital Imaging, 2020, 33 : 168 - 180
12345