Automated Axon Tracking of 3D Confocal Laser Scanning ...

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Track the centerlines in the MIP image as far as possible, (2) switch to cross-sectional tracking ifan axon cross-over is detected and (3) switch back to MIP tracking once the cross-over isresolved. We propose a guided region merging algorithm which is efficient in segmenting theaxons when their boundaries are blurred due to imaging artifacts. Both shape and intensityinformation is used to track the axons which help us segment the axons in the cross-sectionalplane accurately.The organization of the paper is as follows. The method for extracting axon centerlines intwo dimensions is mentioned in Section 2.1. In Section 2.2, we describe the approach adopted tosolve the cross-over problem by switching to the cross-section based approach. Section 2.3introduces our guided region growing algorithm which uses a probabilistic method based on thefeatures extracted from the previous cross-sections.We conclude the paper by demonstrating the results of this algorithm in Section 3. Ouralgorithm is applied on three datasets, and the results are compared with the manual results. Todemonstrate the efficiency of our algorithm, its performance is compared with the centerlineextraction method using repulsive snake model (Cai et al., 2006).2. Materials and MethodsMaterialsThe raw data available for analysis consists of stacks of cross-sectional images of the axonsobtained from neonatal mice using laser scanning confocal microscopes (Olympus FlouviewFV500 and Bio-Rad 1024) equipped with motorized stage. For our study, the neurons of interestall expressed cytoplasmic YFP relatively uniformly inside each cell. YFP was excited with a448nm line of argon laser using an X63 (1.4NA) oil objective with a 520-550nm band-pass6
emission filter (Kasthuri et al., 2003). The images were sampled at the Nyquist limit in theimaging plane (pixel size = 0.1 micron) and over-sampled by a factor of 1.5 in the directionnormal to the imaging plane (optical section thickness = 0.2 micron), and with a 12 bit dynamicrange. Since the axons are not stained, the background is minimal in the acquired images. Thegoal of the developed algorithm is to build a three-dimensional model of the centerlines of theaxons present in the dataset.MethodsThe dataset available for analysis is a sequence of cross-sectional fluorescent microscopicimages of axons. These images can be stacked together to get a better picture of the axonspresent in the dataset. MIP-based tracking algorithms (Can et al., 1999; Zhang et al., 2006) workonly when the axons are well separated, which is often not the case. Thus, axon cross-over isoften encountered when tracking them in two dimensions. Since they never intersect in threedimensions, we resort to a different approach in such special cases. The following flow chartshows the workflow of the algorithm:7
Page 2 and 3: AbstractThis paper presents a new a
Page 4 and 5: the 3D stack. Several automatic alg
Page 8 and 9: 2.1 Tracking the Axons in the MIP I
Page 10 and 11: algorithm, and therefore must be in
Page 12 and 13: towards the center. Similarly, all
Page 14 and 15: algorithm. The blue line in Figure
Page 16 and 17: To avoid this problem, we introduce
Page 18 and 19: (a)(b)(c)Figure 7 - Segmentation of
Page 20 and 21: distribution and used to train the
Page 22 and 23: After the region of interest is ide
Page 24 and 25: The number of cross-sections to be
Page 26 and 27: (e)Figure 10 - Tracking results in
Page 28 and 29: Figure 11 - Tracking results in dat
Page 30 and 31: In comparison with the manual track
Page 32 and 33: algorithm worked for five consecuti
Page 34 and 35: dataset in a direction normal to th
Page 36 and 37: ReferencesAbramoff M.D., Magelhaes,
Page 38: Imaging. 24(12), 1529- 1539.Wang M.

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