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

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The number of cross-sections to be analyzed by the algorithm is assumed to be known by theuser. The user also needs to identify the best MIP image for analysis by looking at the MIPimages along the three orthogonal directions. A rough estimate of the width of the biggest axonin this MIP image has to be specified by the user. The resolution of the grid for the detection ofseed points in the MIP image needs to be defined such that there are at least a few candidate seedpoints on each of the axons in the MIP image. Small values of step size of the MIP-basedtracking algorithm results in higher accuracy in tracking, but increases the execution time of thealgorithm. On the other hand, large values lead to faster tracking, but can result in tracking errorsand a coarse centerline. In all the datasets presented in this paper, this value was set to 3 pixels.Once the algorithm switches to the cross-sectional tracking, the user has to specify the seedpoints for the cross-sections of the axons that have not yet been tracked in the MIP image. This isdone only for the first cross-sectional image where the algorithm switches to a different methodof analysis.The results of our algorithm were validated with the manual reconstruction results that wereobtained using Reconstruct. Finally, the performance of the guided region growing algorithm iscompared with the repulsive snake model on one cross-sectional image from each of the threedatasets.The first dataset contained four axons in total with 256 cross-sectional images, each having aresolution of 141 x 512 pixels. The cross-sectional images were sampled along the Y direction.Figure 10(a)-(b) show the MIP image and the cross-over of axons in the MIP image. Themaximum width of the axon was set to twenty pixels and the resolution of the grid for the MIPimage was set to ten pixels. Figure 10(c) shows the manual reconstruction of the axons. The 2DMIP and the 3D model of the centerlines are shown in Figure 10(d)-(e).24
(a)(b)(c)(d)25
Page 2 and 3: AbstractThis paper presents a new a
Page 4 and 5: the 3D stack. Several automatic alg
Page 6 and 7: Track the centerlines in the MIP im
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 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.

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

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