Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, China
- MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, China
- School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, China
Figures
Figure 1 with 4 supplements
Summary and principle of PointTree.
(A) The reconstruction procedure of PointTree involves the generation, clustering, and connection of foreground points (the first row). Within this procedure, three optimization problems are designed to allocate the foreground points into their respective neurites (the second row). (B) Schematic diagram of information flow score calculation. In a neurite branch with a fixed root node (green circle), the information flow score is calculated based on the assumption that a neurite has few directional changes. The assumption determines the neurite directly connecting to the root node (red), resulting in two branch angles used to calculate the information flow score. (C) Statistical analysis of the consistency between the minimum information flow and the real situation. For 208 neurite branches, the information flow scores are calculated as ground truth according to their manually determined skeletons and root nodes. These scores are then displayed in ascending order. The root nodes of neurite branches are changed to generate both maximum and minimum information flow scores. (D) One neurite branch is decomposed into two by minimizing the total information flow scores. (E) Performance of different methods on separating closely paralleled neurites. In PointTree, a single neurite is represented by a series of ellipsoids whose centerlines are not simultaneously located within different neurites. They are connected using an ellipsoid shape, which results in perfect reconstruction (Left). However, skeleton-based methods fail to separate two closely paralleled neurites due to interference from other signals (Red circle in middle) or connections being interfered with by another neighboring skeleton point (Red circle in right).
Figure 1—figure supplement 1
Comparison of PointTree and several skeleton-based methods for reconstructing the segmented image block derived from ground-truth skeletons.
The ground-truth skeletons are generated using GTree (a semi-automatic software) with manual modification. A series of Gaussian kernels with mean values equal to the coordinates of skeleton points are summed to obtain the corresponding probability image block. The segmented image block is finally generated using a threshold method.
Figure 1—figure supplement 2
The generation of minimal information flow tree.
In (A), the calculation of the information flow score for a branch of neurites with root node 1 is illustrated. The reconstructed skeletons are transformed into a binary structure based on the root node, and the angles with respect to branching nodes labeled with brown circles determine the information flow. These angles will change when the root node changes. The angle of a branching node is formed by its father and child nodes, as exemplified by the N2 node. In (B), the optimization of tree structure to minimize the total information flow score is demonstrated. It shows that decreasing the information flow leads to a more proper tree structure. The second row of (B) provides an example of decomposing tree structure into two individual parts.
Figure 1—figure supplement 3
Post-processing for structures violating the MIFT rule.
(A) MIFT criterion will incorrectly split neurites with sharp directional changes into two branches, but the splitting location is explicitly recorded during this process. (B) Our algorithm searches for connectable neurites around the head nodes identified by MIFT. If no connectable neurites are found for both head nodes, the algorithm will reconnect them based on the recorded splitting points to prevent isolated neurite fragments. (C) presents two real examples violating the MIFT criterion. Through post-processing, PointTree successfully reconnects the split branches back to the correct neurites.
Figure 1—figure supplement 4
Tree structure derived from SWC file that records the axonal reconstruction.
(A) shows an input swc file and its corresponding skeleton structure. (B) shows how the reconstructed skeletons are converted to a binary tree structure.
Figure 2 with 1 supplement
Performance of PointTree on crossover and closely paralleled neurites.
(A) The reconstruction process of crossover and closely paralleled neurites. (B) Quantitative evaluation of PointTree and several skeleton-based methods on identifying closely distributed neurites. The box plots present the statistical information (n=5) in which the horizontal line in the box, the lower and upper borders of the box represent the median value, the first quartile (Q1), and the third quartile (Q3), respectively. The vertical black lines indicate 1.5 × IQR. (C) Three reconstruction examples derived from PointTree and several skeleton-based methods.
Figure 2—figure supplement 1
Visualization of two parallel neurites with different viewing angles.
When the two parallel neurites are in close proximity, they can be distinguished by visualizing them from different angles.
Figure 3
Comparison of reconstruction methods on image blocks containing densely distributed neurites.
(A) Comparison of reconstruction performance among six methods, including PointTree, NGPST, neuTube, APP2, PHDF, and MOST. Individual neurite branches are delineated in different colors. (B) Quantitative evaluation of reconstruction performance using precision, recall, and f1-score. The box plots display these three evaluation indexes (n=8). In the box, the horizontal line represents the median value. The box shows the interquartile range (IQR) from the first quartile (Q1) to the third quartile (Q3). The vertical lines indicate 1.5×IQR.
Figure 4
Reconstruction performance of PointTree across data with different signal-to-noise ratios.
(A) Data blocks from light sheet microscopy (LSM), fluorescent micro-optical sectioning tomography (fMOST), and high-definition fluorescent micro-optical sectioning tomography (HD-fMOST) are selected. SNR and corresponding reconstruction scores with PointTree are drawn with line charts. Each dataset is of sample size n=25 and each data block size of 128×128 × 128. (B) shows reconstruction performance of PointTree on different datasets. (C) The zoomed-in view displays the region marked by white box in the first column of (B), with 25 foreground points and 25 background points sampled respectively. The signal intensities of both the foreground points and background points are plotted in the adjacent line charts.
Figure 5
Minimal information flow tree effectively restrains the accumulation of reconstruction errors.
(A) Reconstruction comparisons in the fusion process with MIFT and without MIFT are shown. Both image blocks and neurite reconstructions are displayed using maximum projection along the z-direction. Two fusion procedures are performed, and the final fusion reconstructions are presented in the third column. (B) The variation in reconstruction accuracy during the fusion process with MIFT and without MIFT is illustrated. Blue points represent the initial reconstruction accuracy from six image blocks, while green points and red points denote the merged reconstruction accuracy with MIFT and without MIFT, respectively. The squares represent the mean values of the evaluation indexes. (C) The skeletons of three neurite branches from the final merged reconstructions with MIFT are shown. Additionally, corresponding ground-truth reconstructions and reconstruction evaluations are also presented.
Figure 6 with 2 supplements
Long-range axonal reconstruction using PointTree.
(A) The image block contains eight neurons in the ventral posteromedial thalamic region. The projection of these neurons includes a large number of densely distributed axons, which are enlarged in A1 and A2. (B) The reconstruction of the eight neurons is achieved by annotators with semi-automatic software GTree, serving as ground-truth reconstruction to evaluate automatic algorithms. The reconstructions B1 and B2 correspond to the image blocks A1 and A2. (C) Automatic reconstruction with PointTree results in reconstructions of the densely distributed axons, which are enlarged in C1 and C2. (D) A comparison between automatic reconstruction and ground-truth reconstruction of axonal projection for one neuron is shown. Green indicates consistent reconstruction, blue indicates missed branches, and red denotes branches from other neurons. (E) Quantitative analysis of long-range projections for these neurons is presented. Statistical information is displayed in boxes (n=8), the horizontal line in the box, the lower and upper borders of the box represent the median value, the first quartile (Q1) and the third quartile (Q3) respectively, the vertical black lines indicate 1.5 × IQR, while black points represent the accuracy of the reconstructions for these neurons.
Figure 6—figure supplement 1
Reconstruction of long-range axonal projections.
The reconstructions were performed using semi-automatic methods with manual modification (GTree, left column) and automatic methods (PointTree, right column). The semi-automatic reconstruction is considered the ground-truth reconstruction for quantifying the accuracy of the automatic reconstruction. In the right column, each panel includes a set of quantitative evaluation indexes in the bottom-right corner, which consist of precision, recall, and f1-score.
Figure 6—figure supplement 2
Reconstruction from different datasets.
Axonal reconstructions were generated from the image blocks (10739×11226 × 3921) collected using the Limo system. The upper portion represents the ground-truth reconstruction, which includes data from 13 neurons. The automatic reconstruction (shown at the bottom) closely matches the ground-truth reconstruction. A quantitative evaluation of the automatic reconstruction is presented in Table 1.
Videos
Tables
Table 1
Quantitative metrics comparing ground truth and reconstructed neurons are presented in Figure 6—figure supplement 2.
| ID | Precision | Recall | F1-Score |
|---|---|---|---|
| 1 | 1.00 | 0.92 | 0.95 |
| 2 | 1.00 | 1.00 | 1.00 |
| 3 | 0.98 | 0.76 | 0.86 |
| 4 | 1.00 | 0.82 | 0.90 |
| 5 | 1.00 | 0.77 | 0.87 |
| 6 | 1.00 | 0.92 | 0.96 |
| 7 | 0.96 | 0.75 | 0.84 |
| 8 | 1.00 | 0.87 | 0.93 |
| 9 | 1.00 | 0.82 | 0.90 |
| 10 | 1.00 | 0.96 | 0.98 |
| 11 | 1.00 | 0.99 | 0.99 |
| 12 | 1.00 | 0.77 | 0.87 |
| 13 | 1.00 | 0.90 | 0.95 |
| 14 | 0.99 | 0.87 | 0.93 |
Table 2
Time cost of three modules in the entire reconstruction for two testing datasets shown in Figure 6, Figure 6—figure supplement 2.
| block number(size: 512×512 × 512) | Points clustering(mins) | Clusters connection(mins) | Reconstruction merging (mins) |
|---|---|---|---|
| 254 | 23 | 18 | 3 |
| 821 | 22 | 35 | 3 |
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Automatic and accurate reconstruction of long-range axonal projections of single-neuron in mouse brain
eLife 13:RP102840.
https://doi.org/10.7554/eLife.102840.3
