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Registration-Based Analysis of the Three-Dimensional Shape of the Retinal Nerve Fiber Layer for Detection of Glaucomatous Defects.

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Paper

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

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The authors' code

Python · 92 lines · 2.8 KB · no license

  1. import sys
  2. import os
  3. from time import time
  5. import numpy as np
  6. import cv2 as cv
  7. from dipy.io.image import save_nifti
  8. from skimage.filters import rank
  9. from skimage.morphology import disk
  10. # Loading the passed arguments
  11. # The correct way to run the script would be something in like
  12. # python create_model_template.py "Z:\Source Files\Automontage Output Files\Python Output Wide Field\OutputFolder" csv_file_path
  13. # or
  14. # python create_model_template.py input_dir
  15. csv_path = None
  16. input_dir = sys.argv[1]
  17. if len(sys.argv) > 2:
  18. csv_path = sys.argv[2]
  20. if os.path.exists(input_dir) == False:
  21. raise Exception("The input directory does not exist!")
  23. if csv_path is None:
  24. subj_list = os.listdir(input_dir)
  25. subj_paths = [os.path.join(input_dir, s) for s in subj_list]
  27. else:
  28. subj_list = []
  29. subj_paths = []
  30. import csv
  31. with open(csv_path, mode='r') as csvfile:
  32. csvreader = csv.reader(csvfile)
  33. for idx, row in enumerate(csvreader):
  34. if idx == 0:
  35. continue
  36. subj_id = row[0]
  37. image_n = row[1]
  38. image_id = row[2]
  39. eye = row[3]
  40. subj_list.append(subj_id+'_'+image_n+'_'+eye)
  41. subj_paths.append(os.path.join(input_dir, 'ID_'+subj_id+'_'+image_n, eye, 'AC_'+image_id))
  43. model_volume = None
  44. s_idx = 0
  45. for sub_path, sub_dir in zip(subj_paths, subj_list):
  46. s_t = time()
  48. # Note that we are using a fixed shape here
  49. # The numbers might have to change if applied on a different site
  50. # As long as there is sufficient depth to normalize the raw data
  51. # it should be fine.
  52. volume = []
  54. # Load and normalize/blur volume
  55. for i in range(0, 164, 4):
  56. try:
  57. image = cv.imread(os.path.join(sub_path, sub_dir+'_NormalizedILM_'+str(i)+'.jpg'), 0)
  58. except:
  59. image = cv.imread(os.path.join(sub_path, sub_dir+'_NormalizedILM_'+str(i)+'.png'), 0)
  60. volume.append(image)
  62. volume = np.stack(volume, axis=-1)
  63. for i in range(0, 164, 4):
  64. image = volume[..., i//4]
  65. image = rank.mean_percentile(image, footprint=disk(7), p0=.1, p1=.9)
  66. volume[..., i//4] = image
  67. if sub_dir.endswith('OD'):
  68. volume = np.flip(volume, axis=1)
  70. image = volume / np.max(volume)
  71. image = np.nan_to_num(image)
  73. if model_volume is None:
  74. shape = image.shape
  75. model_volume = np.zeros(shape+(len(subj_paths),))
  77. model_volume[..., s_idx] = image
  78. s_idx += 1
  80. model = np.zeros(shape)
  81. for i in range(shape[0]):
  82. for j in range(shape[1]):
  83. for k in range(shape[2]):
  84. line = model_volume[i, j, k]
  85. try:
  86. model[i, j, k] = np.median(line[line!=0])
  87. except:
  88. model[i, j, k] = 0
  90. model = np.nan_to_num(model)
  92. save_nifti('model_volume.nii.gz', model, np.eye(4))

create_model_template.py at commit 66decdb, no license · at the source

Overview

Authors: Jong Sung Park1, Brett J. King2, Eleftherios Garyfallidis1, William H. Swanson2
  1. Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
  2. School of Optometry, Indiana University, Bloomington, IN, USA
Institutions: Indiana University Bloomington (United States); Indiana University (United States)
Journal: Translational vision science & technology, volume 15, issue 3, article 8
Dates: received 25 July 2025; accepted 27 January 2026; published online 9 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1167/tvst.15.3.8 · PMID 41800848 · PMCID PMC12988675 · OpenAlex W7134230838
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), clinical / translational (subfield)
Methods: Connectivity, fMRI & imaging
Keywords: glaucoma, nerve fiber layer, registration
MeSH: Glaucoma*, Imaging, Three-Dimensional*, Nerve Fibers*, Retinal Ganglion Cells*, Tomography, Optical Coherence*, Aged, Female, Humans, Male, Middle Aged, Visual Field Tests, Visual Fields (* major topic)
Journal subjects: Glaucoma
Topic: Glaucoma and retinal disorders (Ophthalmology, Medicine), according to OpenAlex
Funding: NEI NIH HHS (R01 EY024542)
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

pjsjongsung/detect_defect

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 66decdb92aca13d82c5dd6472ec3903c34ff08a2, 30 September 2025
Languages: Python (2)
Size: 4 files, 2 scripts
Software Heritage: not archived
Found in: the text, “Discussion”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: DIPY (2 files), NumPy (2 files), OpenCV (2 files), scikit-image (2 files), Matplotlib (1 file), SciPy (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/pjsjongsung/detect_defect

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 2 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • 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

No dataset and no data link were found in the paper.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 3 keywords, 12 MeSH terms, 1 funder, 37 references.

Cite

This paper

Park, J. S., King, B. J., Garyfallidis, E., & Swanson, W. H. (2026). Registration-Based Analysis of the Three-Dimensional Shape of the Retinal Nerve Fiber Layer for Detection of Glaucomatous Defects. Translational vision science & technology, 15(3), 8. https://doi.org/10.1167/tvst.15.3.8

BibTeX

@article{park2026registration,
author = {Park, Jong Sung and King, Brett J. and Garyfallidis, Eleftherios and Swanson, William H.},
title = {{Registration-Based Analysis of the Three-Dimensional Shape of the Retinal Nerve Fiber Layer for Detection of Glaucomatous Defects}},
journal = {Translational vision science \& technology},
year = {2026},
month = mar,
volume = {15},
number = {3},
pages = {8},
publisher = {Association for Research in Vision and Ophthalmology},
issn = {2164-2591},
doi = {10.1167/tvst.15.3.8},
url = {https://doi.org/10.1167/tvst.15.3.8},
pmid = {41800848},
pmcid = {PMC12988675}
}

RIS

TY - JOUR
AU - Park, Jong Sung
AU - King, Brett J.
AU - Garyfallidis, Eleftherios
AU - Swanson, William H.
TI - Registration-Based Analysis of the Three-Dimensional Shape of the Retinal Nerve Fiber Layer for Detection of Glaucomatous Defects
T2 - Translational vision science & technology
J2 - Transl Vis Sci Technol
PY - 2026
DA - 2026/03/01
VL - 15
IS - 3
SP - 8
SN - 2164-2591
PB - Association for Research in Vision and Ophthalmology
DO - 10.1167/tvst.15.3.8
UR - https://doi.org/10.1167/tvst.15.3.8
LA - en
ER -

CSL-JSON

{
"id": "10.1167/tvst.15.3.8",
"type": "article-journal",
"title": "Registration-Based Analysis of the Three-Dimensional Shape of the Retinal Nerve Fiber Layer for Detection of Glaucomatous Defects",
"container-title": "Translational vision science & technology",
"author": [
{
"family": "Park",
"given": "Jong Sung"
},
{
"family": "King",
"given": "Brett J."
},
{
"family": "Garyfallidis",
"given": "Eleftherios"
},
{
"family": "Swanson",
"given": "William H."
}
],
"container-title-short": "Transl Vis Sci Technol",
"volume": "15",
"issue": "3",
"page": "8",
"DOI": "10.1167/tvst.15.3.8",
"PMID": "41800848",
"PMCID": "PMC12988675",
"ISSN": "2164-2591",
"publisher": "Association for Research in Vision and Ophthalmology",
"URL": "https://doi.org/10.1167/tvst.15.3.8",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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