Figure 4: Evaluation of CDA.

(a) CDA generates consistent 3D neurite tracts (curves) (green and blue) that are very close to the ground truth (red) regardless of different viewing angles. Image: 3D confocal image of a heavy-noise-contaminated dragonfly thoracic ganglion neuron. The ‘ground–truth’ curves were generated using Vaa3D-Neuron1 (ref. 8) and were also manually inspected to ensure that they are correct. (b) Distances between the 3D neurite tracts (curves), which are generated from different angles and different zooms, and the ground truth. Data is based on 1,470 measurements of 7 tracts in the image in a. (c) Percentages of curve knots that have visible spatial difference (≥ 2 voxels) (mean±s.d.). 2D/2.5D: manual generation of a 3D curve based on first mouse-clicking on 2D cross-sectional XY planes in a 3D image, or using all three XY, YZ and ZX cross-sectional planes (2.5D), and then concatenating these locations sequentially. 3D PPA: manual generation of a 3D curve based on first mouse-clicking in the 3D-rendered image using PPA to produce a series of 3D locations, and then concatenating them. Data are based on tracing the primary projection tracts in five 3D dragonfly confocal images where the curve generation is possible for all the 2D/2.5D, 3D PPA and 3D CDA methods. (d) Speed of 3D curve generation using different methods (mean±s.d.). c-time, computing time for CDA; t-time, total time (including human-machine interaction and c-time) for CDA. Image data are the same in c.