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Volume-inflation registration (INFREG) for morphometric analysis of human focal cortical dysplasia type II.

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. [1] § STAR★Methods › Method details › Neuroimaging normalization ↔ src/MRI_func_brain.m, lines 111–136 · score 0.60 · intensity normalization, SANLM, nonlocal, denoising, CAT, filtering
  2. [2] § STAR★Methods › Method details › Assessment of feature maps for FCD lesions diagnosis and prediction ↔ vox2organ/utils/eval_metrics.py, lines 176–207 · score 0.57 · Dice score, ground truth, metrics, predicting

Paper

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The authors' code

MATLAB · 169 lines · 4.5 KB · no license · 1 match

  1. function [varargout] = MRI_func_brain(sMRI,func,varargin)
  2. %UNTITLED2 此处显示有关此函数的摘要
  3. % 此处显示详细说明
  5. sMRI = MRI_read_Data(sMRI,'verbose',0);
  6. switch func
  7. case 'seg_kmeans'
  8. output = mri_seg_kmeans(sMRI,varargin{:});
  9. case 'rand_crop'
  10. output = mri_randcrop(sMRI,varargin{:});
  11. case 'bw_seedgrowth'
  12. output = mri_bw_seedgrowth(sMRI,varargin{:});
  13. case 'directional_smooth'
  14. output = mri_directional_smooth(sMRI,varargin{:});
  15. case 'denoise'
  16. output = mri_denoise(sMRI);
  17. case 'pvcnorm'
  18. output = mri_pvcnorm(sMRI,varargin{:});
  19. end
  21. if ~iscell(output)
  22. output = {output};
  23. end
  24. varargout = output;
  25. end
  27. function sImg = mri_seg_kmeans(sMRI,varargin)
  28. options_default = structure('N',3,'mask',[]);
  29. [options, eval_str] = resolve_input(options_default,varargin);
  30. eval(eval_str);
  32. temp = single(sMRI.data);
  34. if ~isempty(mask)
  35. [L,L_cen] = imsegkmeans3(temp(mask>0),3);
  36. L_cen = sort(L_cen);
  37. else
  38. [L,L_cen] = imsegkmeans3(temp,N);
  39. [L_cen,idx] = sort(L_cen);
  40. [~,idx] = sort(idx);
  41. L = reshape(idx(L),size(L));
  42. end
  44. for i = 1:3
  45. L_frac{i} = power(exp(1),-2*abs((temp-L_cen(i))));
  46. end
  48. L_sum = L_frac{1}+L_frac{2}+L_frac{3};
  49. for i = 1:3
  50. temp = L_frac{i}./L_sum;
  51. csf_mean = mean(temp(mask==0),"omitnan");
  52. temp = (temp - csf_mean)./(1-csf_mean); temp(temp<0) = 0;
  53. L_frac{i} = temp;
  54. end
  55. end
  57. function [out] = mri_randcrop(img,varargin)
  58. options_default = structure('mask',[],'sz',50);
  59. [options, eval_str] = resolve_input(options_default,varargin);
  60. eval(eval_str);
  62. if isstruct(img)
  63. img = img.data;
  64. end
  66. if length(sz) == 1
  67. sz = [sz,sz];
  68. end
  70. mask(1:round(sz(1)/2),:) = 0; mask(:,1:round(sz(2)/2)) = 0;
  71. mask(end-round(sz(1)/2):end,:) = 0; mask(:,end-round(sz(2)/2):end) = 0;
  73. idx = find(mask>0);
  74. crop_idx = idx(randi(length(idx),1));
  76. [y,x] = ind2sub(size(img),crop_idx);
  77. crop_loc = [y,x] - round(sz/2)-1;
  79. img_crop = img(crop_loc(1)+[1:sz(1)],crop_loc(2)+[1:sz(2)]);
  81. out = {img_crop,crop_loc,crop_idx};
  82. end
  84. function bw_grow = mri_bw_seedgrowth(bw,varargin)
  85. % 从一个种子点寻找邻域（如沿着灰白质边界走
  86. options_default = structure('seed',[],'N',5);
  87. [options, eval_str] = resolve_input(options_default,varargin);
  88. eval(eval_str);
  90. temp = single(bw);
  91. temp(seed(1),seed(2)) = 100; % 设为一个大值
  92. bw_grow = 0*single(bw);
  94. for i = 1:N
  95. temp = imfilter(temp,ones(3),'same').*bw;
  96. temp(temp>20) = 100; temp(temp>0 & temp<20) = 1;
  97. bw_grow(temp>1) = bw_grow(temp>1)+1;
  98. end
  99. end
  101. function img_filt = mri_directional_smooth(img,Gx0,Gy0,sm_mask,N)
  102. img_filt = img;
  103. for i = 1:N
  104. img_smx = imfilter(img_filt,[1/2 -1 1/2],'replicate');
  105. img_smy = imfilter(img_filt,[1/2;-1;1/2],'replicate');
  106. temp = img_smx.*Gx0+img_smy.*Gy0;
  107. img_filt = img_filt + temp.*sm_mask;
  108. end
  109. end
  111. function output = mri_denoise(sMRI)
  112. % from cat12: cat_vol_sanlm.m
  113. % nonlocal means for denoising
  114. job.intlim = [1,1]-0.0001;
  115. job.rician = 0;
  117. src = single(sMRI.data);
  119. % histogram limit
  120. [src,srcth] = cat_stat_histth(src,job.intlim);
  122. % use intensity normalisation because cat_sanlm did not filter values below ~0.01
  123. th = max( cat_stat_nanmean( src(src(:)>cat_stat_nanmean(src(src(:)>0))) ) , ...
  124. abs(cat_stat_nanmean( src(src(:)<abs(cat_stat_nanmean(src(src(:)<0)))))) );
  126. src = (src / th) * 100;
  127. src = (src - srcth(1)); % avoid negative values!
  128. cat_sanlm(src,3,1,job.rician);
  129. src = src + srcth(1); % restore original intensity range
  130. src = (src / 100) * th;
  132. sMRI_filt = sMRI;
  133. sMRI_filt.data = src;
  135. output = {sMRI_filt};
  136. end
  138. function output = mri_pvcnorm(sMRI,varargin)
  139. options_default = structure('label',[]);
  140. [options, eval_str] = resolve_input(options_default,varargin);
  141. eval(eval_str);
  143. % from % [Ym] = cat_main_gintnorm1639(single(T1_raw.data),Tth);
  144. Ysrc = single(sMRI.data);
  145. if isequal(sMRI.size,size(label))
  146. T3th = 0; T3thx = 0;
  147. for i = 1:3
  148. T3th(i+1) = median(Ysrc(abs(label - i) < 0.3));
  149. T3thx(i+1) = i;
  150. end
  151. else
  152. T3th = label.T3th;
  153. T3thx = label.T3thx;
  154. end
  156. Ym = 0*Ysrc;
  157. for i=2:4
  158. M = Ysrc>T3th(i-1) & Ysrc<=T3th(i);
  159. Ym(M(:)) = T3thx(i-1) + (Ysrc(M(:)) - T3th(i-1))/diff(T3th(i-1:i))*diff(T3thx(i-1:i));
  160. end
  161. M = Ysrc>=T3th(end);
  162. Ym(M(:)) = T3thx(i) + (Ysrc(M(:)) - T3th(i))/diff(T3th(end-1:end))*diff(T3thx(i-1:i));
  163. Ym = Ym / 3;
  165. sMRI_norm = sMRI;
  166. sMRI_norm.data = Ym;
  168. output = {sMRI_norm,T3th};
  169. end

MRI_func_brain.m at commit 9568fdd, no license · at the source

Overview

Authors: Yongsheng Zhang1, Haojie Han1, Fang Chen2, Hongen Liao1,2
ORCID iDs: Yongsheng Zhang
  1. School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
  2. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Institutions: Tsinghua University (China); Shanghai Jiao Tong University (China)
Journal: iScience, volume 29, issue 9, article 117446
Dates: received 31 October 2025; accepted 20 August 2026; published online 9 September 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1016/j.isci.2026.117446 · PMID 42755994 · PMCID PMC13582014 · OpenAlex W7212078393
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), epilepsy (population)
Methods: Connectivity, Statistics, Spectral & time-frequency, Machine learning, fMRI & imaging
Keywords: brain, registration, inflation, focal cortical dysplasia, voxel-based morphometry
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: National Natural Science Foundation of China (U22A2051, 82027807, 62201315); Science and Technology Commission of Shanghai Municipality (24511104100); National Key Research and Development Program of China (2022YFC- 2405200)
Citations: not cited yet (Europe PMC); 56 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.

Repositories

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

ifsunrise/brain-inflate-registration

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 9568fddc506662d40ae9f088951b3494efb24bee, 26 July 2026
Languages: MATLAB (24)
Size: 26 files, 24 scripts
Software Heritage: not archived
Found in: “Data and code availability”
Holds: README, 11 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Image Processing Toolbox (10 files), Brainstorm (1 file), CAT12 (1 file), Parallel Computing Toolbox (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
25 files
At the source: github.com/ifsunrise/brain-inflate-registration

ai-med/Vox2Cortex

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: ef10ca2cf9999e1698a98a4bf9dce19b0000bd10, 21 May 2025
Languages: Python (49), Shell (3), Jupyter (2)
Size: 93 files, 54 scripts
Software Heritage: not archived
Found in: the resources table
Holds: README, license file, environment (docker/Dockerfile), 2 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: PyTorch (27 files), NumPy (24 files), NiBabel (16 files), Matplotlib (5 files), scikit-image (5 files), pandas (4 files), SciPy (3 files), FreeSurfer (2 files), PyTorch Geometric (1 file), SimpleITK (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
56 files
At the source: github.com/ai-med/Vox2Cortex

The paper's code and data availability statement is in the Data section.

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;
  • 78 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.

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Data

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Availability statements

The paper has a data availability statement and a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing them here; in short, from what the harvester recognized in them:

Read them in the paper: doi.org/10.1016/j.isci.2026.117446.

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Version 3, 28 September 2026

  • Authors: added Yongsheng Zhang (0000-0002-3086-2168); removed Yongsheng Zhang
  • Funding: added National Natural Science Foundation of China: U22A2051, 82027807, 62201315; Science and Technology Commission of Shanghai Municipality: 24511104100; National Key Research and Development Program of China: 2022YFC- 2405200

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 56 references.

Cite

This paper

Zhang, Y., Han, H., Chen, F., & Liao, H. (2026). Volume-inflation registration (INFREG) for morphometric analysis of human focal cortical dysplasia type II. iScience, 29(9), 117446. https://doi.org/10.1016/j.isci.2026.117446

BibTeX

@article{zhang2026volume,
author = {Zhang, Yongsheng and Han, Haojie and Chen, Fang and Liao, Hongen},
title = {{Volume-inflation registration (INFREG) for morphometric analysis of human focal cortical dysplasia type II}},
journal = {iScience},
year = {2026},
month = sep,
volume = {29},
number = {9},
pages = {117446},
publisher = {Elsevier},
issn = {2589-0042},
doi = {10.1016/j.isci.2026.117446},
url = {https://doi.org/10.1016/j.isci.2026.117446},
pmid = {42755994},
pmcid = {PMC13582014}
}

RIS

TY - JOUR
AU - Zhang, Yongsheng
AU - Han, Haojie
AU - Chen, Fang
AU - Liao, Hongen
TI - Volume-inflation registration (INFREG) for morphometric analysis of human focal cortical dysplasia type II
T2 - iScience
J2 - iScience
PY - 2026
DA - 2026/09/09
VL - 29
IS - 9
SP - 117446
SN - 2589-0042
PB - Elsevier
DO - 10.1016/j.isci.2026.117446
UR - https://doi.org/10.1016/j.isci.2026.117446
LA - en
ER -

CSL-JSON

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