3d axon structure extraction and analysis in confocal fluorescence microscopy images

  • Authors:

  • Yong Zhang;Xiaobo Zhou;Ju Lu;Jeff Lichtman;Donald Adjeroh;Stephen T. C. Wong

  • Affiliations:

  • Center of Biomedical Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, U.S.A. yzhang@tmhs.org;Center of Biomedical Informatics, Department of Radiology, The Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, U.S.A. XZhou@tmhs.org;Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, U.S.A. jlu@mcb.harvard.edu;Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, U.S.A. jeff@mcb.harvard.edu;Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, U.S.A. donald.adjeroh@mail.wvu.edu;Center of Biomedical Informatics, Department of Radiology, Methodist Hospital Research Institute, Weill Cornell Medical College, Houston, TX 77030, U.S.A. STWong@tmhs.org

  • Venue:
  • Neural Computation
  • Year:
  • 2008

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Abstract

The morphological properties of axons, such as their branching patterns and oriented structures, are of great interest for biologists in the study of the synaptic connectivity of neurons. In these studies, researchers use triple immunofluorescent confocal microscopy to record morphological changes of neuronal processes. Three-dimensional (3D) microscopy image analysis is then required to extract morphological features of the neuronal structures. In this article, we propose a highly automated 3D centerline extraction tool to assist in this task. For this project, the most difficult part is that some axons are overlapping such that the boundaries distinguishing them are barely visible. Our approach combines a 3D dynamic programming (DP) technique and marker-controlled watershed algorithm to solve this problem. The approach consists of tracking and updating along the navigation directions of multiple axons simultaneously. The experimental results show that the proposed method can rapidly and accurately extract multiple axon centerlines and can handle complicated axon structures such as cross-over sections and overlapping objects.