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Reconstruction of Axonal Projections of Single Neurons Using PointTree.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches · 1 of them tie a paragraph to a whole file, not to given lines: a weak match, whose lines are not tinted
  1. [1] § Procedure ↔ BigImgPredict_bak.py, the whole file · a weak match · score 0.59 · imgPath, modelPath, savePath, segmentation
  2. [2] § Procedure ↔ BigImgPredict.py, lines 15–68 · score 0.58 · imgPath, modelPath, savePath

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 78 lines · 3.2 KB · MIT · 1 match

  1. '''大图预测'''
  2. import json
  3. import os
  4. import shutil
  6. os.environ['KMP_DUPLICATE_LIB_OK']='True'
  7. import time, torch
  8. import numpy as np
  9. import tifffile, os
  10. from os.path import join
  11. from ModelPredictPy import ModelPredictClass
  13. if __name__ == '__main__':
  14. imgPath = r"D:\qxz\MyProject\KKMarkCellBody\Code\BrainSegNewQxz\SegmentProblem\DataSet\img"
  15. modelPath = r'./ModelSave/exp006/supernet_00010.pth'
  16. # testTxt = r'test.txt'
  17. savePath = r'D:\qxz\MyProject\KKMarkCellBody\Code\BrainSegNewQxz\SegmentProblem\DataSet\mask'
  18. bigImgSize = np.array([512, 512, 512], dtype=np.int32)
  19. imgSize = np.array([128, 128, 128], dtype=np.int32)
  20. batchSize = 1
  21. fieldLen = 16
  22. device = torch.device('cuda:0')
  24. # cfgPath = join(savePath, 'info.json')
  25. # saveRes = join(savePath, 'result')
  27. # if os.path.isdir(savePath): shutil.rmtree(savePath)
  28. # os.makedirs(savePath)
  29. # if os.path.isdir(saveRes): shutil.rmtree(saveRes)
  30. # os.makedirs(saveRes)
  32. saveRes = savePath
  33. os.makedirs(saveRes, exist_ok=True)
  35. model = ModelPredictClass(modelPath, fieldLen=fieldLen, device=device) # 预测类
  36. # with open(join(rootPath, testTxt), 'r') as f:
  37. # ls = f.read().strip().split('\n')
  38. # imgPath = join(rootPath, 'images')
  39. ls = os.listdir(imgPath)
  41. saveInfo = []
  42. for name in ls:
  43. oriImg = tifffile.imread(join(imgPath, name))
  44. if oriImg.ndim != 3: continue
  45. # oriImg_eq = exposure.equalize_hist(oriImg, nbins=oriImg.max() - oriImg.min())
  46. # newImg = np.zeros_like(oriImg)
  47. maskBigImg = np.zeros(bigImgSize[::-1], dtype=np.uint8)
  48. sliceNumber = bigImgSize // imgSize
  49. count = 0
  50. newName = os.path.splitext(name)[0]
  51. for nz in range(sliceNumber[2]):
  52. for ny in range(sliceNumber[1]):
  53. for nx in range(sliceNumber[0]):
  54. s1 = time.time()
  55. sp = imgSize * [nx, ny, nz]
  56. ep = sp + imgSize
  57. img = oriImg[sp[2]: ep[2], sp[1]: ep[1], sp[0]: ep[0]]
  58. # img_eq = oriImg_eq[sp[2]: ep[2], sp[1]: ep[1], sp[0]: ep[0]]
  59. # img = np.array([img, img_eq], dtype=np.float32)
  61. # newImg[sp[2]: ep[2], sp[1]: ep[1], sp[0]: ep[0]] = img
  62. mask = model(img)
  63. maskBigImg[sp[2]: ep[2], sp[1]: ep[1], sp[0]: ep[0]] = mask
  64. print(count, time.time() - s1)
  65. count += 1
  66. # tifffile.imwrite(join(savePath, '%s_%s_0.tif' % (newName, str(count).zfill(5))), img)
  67. # tifffile.imwrite(join(savePath, '%s_%s_1.tif' % (newName, str(count).zfill(5))), mask)
  68. # print('%s_%s_1.tif' % (newName, str(count).zfill(5)), mask.sum())
  70. # break
  71. # break
  72. # break
  73. # tifffile.imwrite(join(savePath, '%s_0.tif' % newName), oriImg[:bigImgSize[2], :bigImgSize[1], :bigImgSize[0]])
  74. maskBigImg[maskBigImg < 103] = 0
  75. tifffile.imwrite(join(saveRes, '%s.tif' % newName), maskBigImg)
  76. # saveInfo.append([join(saveRes, '%s.tif' % newName), join(imgPath, name), ''])
  77. # with open(cfgPath, 'w') as f:
  78. # f.write(json.dumps(saveInfo))

BigImgPredict_bak.py at commit d85598b, under MIT · at the source

Overview

Authors: Lin Cai1,2, Xuzhong Qu1,2, Junwei Wang1,2, Yuan Shen1,2, Tingwei Quan1,2
  1. Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China
  2. MOE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China
Institutions: Wuhan National Laboratory for Optoelectronics (China); Huazhong University of Science and Technology (China)
Journal: Bio-protocol, volume 16, issue 5, article e5616
Dates: received 3 December 2025; accepted 27 January 2026; published online 5 March 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI 10.21769/bioprotoc.5616 · PMID 41847379 · PMCID PMC12989286 · OpenAlex W7128082106
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Methods: Smoothing, state filtering, decompositions
Keywords: Neuron reconstruction, Axonal projections, Points assignments, Dense reconstruction, Brain circuit mapping
Topic: Cell Image Analysis Techniques (Biophysics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (32471146)
Citations: not cited yet (Europe PMC); 11 references in the paper

Abstract

The morphology of single-neuron axonal projections is critical for deciphering neural circuitry and information flow in the brain. Yet, manually reconstructing these complex, long-range projections from high-throughput whole-brain imaging data remains an exceptionally labor-intensive and time-consuming task. Here, we developed a points assignment-based method for axonal reconstruction, named PointTree. PointTree enables the precise identification of the individual axons from densely packed axonal population using a minimal information flow tree model to suppress the snowball effect of reconstruction errors. In this protocol, we have elaborated on how to configure the required environment for PointTree software, prepare suitable data for it, and run the software. This protocol can assist neuroscience researchers in more easily and rapidly obtaining the reconstruction results of neuronal axons.

Key features

• Optimized for mapping long-range axons that connect distant brain regions in dense or crossover scenarios.

• Enables high-fidelity (F1-score > 80%) reconstruction of hundreds of GB of large-volume imaging data.

• Compatible with LSM, fMOST, and HD-fMOST systems for diverse neuroimaging datasets.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

FateUBW0227/Seg_Net

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d85598bbd0e7abe42c6c19c956eac22e78cb4594, 20 January 2026
Languages: Python (24)
Size: 44 files, 24 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (18 files), PyTorch (16 files), tifffile (8 files), Matplotlib (1 file), OpenCV (1 file), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
26 files
At the source: github.com/FateUBW0227/Seg_Net

gtreesoftware/gtree

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: d5dc04e9cf74b874be27de1683ef466d8cb7ae57, 13 January 2021
Languages: Python (1)
Size: 42 files, 1 script
Software Heritage: not archived
Found in: the text, “Procedure”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
3 files
At the source: github.com/GTreeSoftware/GTree/wiki

Tracing map

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  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 25 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 1 funder, 11 references.

Cite

This paper

Cai, L., Qu, X., Wang, J., Shen, Y., & Quan, T. (2026). Reconstruction of Axonal Projections of Single Neurons Using PointTree. Bio-protocol, 16(5), e5616. https://doi.org/10.21769/bioprotoc.5616

BibTeX

@article{cai2026reconstruction,
author = {Cai, Lin and Qu, Xuzhong and Wang, Junwei and Shen, Yuan and Quan, Tingwei},
title = {{Reconstruction of Axonal Projections of Single Neurons Using PointTree}},
journal = {Bio-protocol},
year = {2026},
month = mar,
volume = {16},
number = {5},
pages = {e5616},
publisher = {Bio-protocol, LLC},
issn = {2331-8325},
doi = {10.21769/bioprotoc.5616},
url = {https://doi.org/10.21769/bioprotoc.5616},
pmid = {41847379},
pmcid = {PMC12989286}
}

RIS

TY - JOUR
AU - Cai, Lin
AU - Qu, Xuzhong
AU - Wang, Junwei
AU - Shen, Yuan
AU - Quan, Tingwei
TI - Reconstruction of Axonal Projections of Single Neurons Using PointTree
T2 - Bio-protocol
J2 - Bio Protoc
PY - 2026
DA - 2026/03/05
VL - 16
IS - 5
SP - e5616
SN - 2331-8325
PB - Bio-protocol, LLC
DO - 10.21769/bioprotoc.5616
UR - https://doi.org/10.21769/bioprotoc.5616
LA - en
ER -

CSL-JSON

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"id": "10.21769/bioprotoc.5616",
"type": "article-journal",
"title": "Reconstruction of Axonal Projections of Single Neurons Using PointTree",
"container-title": "Bio-protocol",
"author": [
{
"family": "Cai",
"given": "Lin"
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{
"family": "Qu",
"given": "Xuzhong"
},
{
"family": "Wang",
"given": "Junwei"
},
{
"family": "Shen",
"given": "Yuan"
},
{
"family": "Quan",
"given": "Tingwei"
}
],
"container-title-short": "Bio Protoc",
"volume": "16",
"issue": "5",
"page": "e5616",
"DOI": "10.21769/bioprotoc.5616",
"PMID": "41847379",
"PMCID": "PMC12989286",
"ISSN": "2331-8325",
"publisher": "Bio-protocol, LLC",
"URL": "https://doi.org/10.21769/bioprotoc.5616",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
5
]
]
}
}

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