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

Paper

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The authors' code

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

function [varargout] = MRI_func_brain(sMRI,func,varargin)
%UNTITLED2 此处显示有关此函数的摘要
% 此处显示详细说明
sMRI = MRI_read_Data(sMRI,'verbose',0);
switch func
case 'seg_kmeans'
output = mri_seg_kmeans(sMRI,varargin{:});
case 'rand_crop'
output = mri_randcrop(sMRI,varargin{:});
case 'bw_seedgrowth'
output = mri_bw_seedgrowth(sMRI,varargin{:});
case 'directional_smooth'
output = mri_directional_smooth(sMRI,varargin{:});
case 'denoise'
output = mri_denoise(sMRI);
case 'pvcnorm'
output = mri_pvcnorm(sMRI,varargin{:});
end
if ~iscell(output)
output = {output};
end
varargout = output;
end
function sImg = mri_seg_kmeans(sMRI,varargin)
options_default = structure('N',3,'mask',[]);
[options, eval_str] = resolve_input(options_default,varargin);
eval(eval_str);
temp = single(sMRI.data);
if ~isempty(mask)
[L,L_cen] = imsegkmeans3(temp(mask>0),3);
L_cen = sort(L_cen);
else
[L,L_cen] = imsegkmeans3(temp,N);
[L_cen,idx] = sort(L_cen);
[~,idx] = sort(idx);
L = reshape(idx(L),size(L));
end
for i = 1:3
L_frac{i} = power(exp(1),-2*abs((temp-L_cen(i))));
end
L_sum = L_frac{1}+L_frac{2}+L_frac{3};
for i = 1:3
temp = L_frac{i}./L_sum;
csf_mean = mean(temp(mask==0),"omitnan");
temp = (temp - csf_mean)./(1-csf_mean); temp(temp<0) = 0;
L_frac{i} = temp;
end
end
function [out] = mri_randcrop(img,varargin)
options_default = structure('mask',[],'sz',50);
[options, eval_str] = resolve_input(options_default,varargin);
eval(eval_str);
if isstruct(img)
img = img.data;
end
if length(sz) == 1
sz = [sz,sz];
end
mask(1:round(sz(1)/2),:) = 0; mask(:,1:round(sz(2)/2)) = 0;
mask(end-round(sz(1)/2):end,:) = 0; mask(:,end-round(sz(2)/2):end) = 0;
idx = find(mask>0);
crop_idx = idx(randi(length(idx),1));
[y,x] = ind2sub(size(img),crop_idx);
crop_loc = [y,x] - round(sz/2)-1;
img_crop = img(crop_loc(1)+[1:sz(1)],crop_loc(2)+[1:sz(2)]);
out = {img_crop,crop_loc,crop_idx};
end
function bw_grow = mri_bw_seedgrowth(bw,varargin)
% 从一个种子点寻找邻域（如沿着灰白质边界走
options_default = structure('seed',[],'N',5);
[options, eval_str] = resolve_input(options_default,varargin);
eval(eval_str);
temp = single(bw);
temp(seed(1),seed(2)) = 100; % 设为一个大值
bw_grow = 0*single(bw);
for i = 1:N
temp = imfilter(temp,ones(3),'same').*bw;
temp(temp>20) = 100; temp(temp>0 & temp<20) = 1;
bw_grow(temp>1) = bw_grow(temp>1)+1;
end
end
function img_filt = mri_directional_smooth(img,Gx0,Gy0,sm_mask,N)
img_filt = img;
for i = 1:N
img_smx = imfilter(img_filt,[1/2 -1 1/2],'replicate');
img_smy = imfilter(img_filt,[1/2;-1;1/2],'replicate');
temp = img_smx.*Gx0+img_smy.*Gy0;
img_filt = img_filt + temp.*sm_mask;
end
end
function output = mri_denoise(sMRI)
% from cat12: cat_vol_sanlm.m
% nonlocal means for denoising
job.intlim = [1,1]-0.0001;
job.rician = 0;
src = single(sMRI.data);
% histogram limit
[src,srcth] = cat_stat_histth(src,job.intlim);
% use intensity normalisation because cat_sanlm did not filter values below ~0.01
th = max( cat_stat_nanmean( src(src(:)>cat_stat_nanmean(src(src(:)>0))) ) , ...
abs(cat_stat_nanmean( src(src(:)<abs(cat_stat_nanmean(src(src(:)<0)))))) );
src = (src / th) * 100;
src = (src - srcth(1)); % avoid negative values!
cat_sanlm(src,3,1,job.rician);
src = src + srcth(1); % restore original intensity range
src = (src / 100) * th;
sMRI_filt = sMRI;
sMRI_filt.data = src;
output = {sMRI_filt};
end
function output = mri_pvcnorm(sMRI,varargin)
options_default = structure('label',[]);
[options, eval_str] = resolve_input(options_default,varargin);
eval(eval_str);
% from % [Ym] = cat_main_gintnorm1639(single(T1_raw.data),Tth);
Ysrc = single(sMRI.data);
if isequal(sMRI.size,size(label))
T3th = 0; T3thx = 0;
for i = 1:3
T3th(i+1) = median(Ysrc(abs(label - i) < 0.3));
T3thx(i+1) = i;
end
else
T3th = label.T3th;
T3thx = label.T3thx;
end
Ym = 0*Ysrc;
for i=2:4
M = Ysrc>T3th(i-1) & Ysrc<=T3th(i);
Ym(M(:)) = T3thx(i-1) + (Ysrc(M(:)) - T3th(i-1))/diff(T3th(i-1:i))*diff(T3thx(i-1:i));
end
M = Ysrc>=T3th(end);
Ym(M(:)) = T3thx(i) + (Ysrc(M(:)) - T3th(i))/diff(T3th(end-1:end))*diff(T3thx(i-1:i));
Ym = Ym / 3;
sMRI_norm = sMRI;
sMRI_norm.data = Ym;
output = {sMRI_norm,T3th};
end

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

Overview

Authors: Yongsheng Zhang¹, Haojie Han¹, Fang Chen², Hongen Liao^1,2

ORCID iDs: Yongsheng Zhang

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 (how the code is found and 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 (Oncology, 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

mlx livescripts/FCD_Process_map18.mlx, MATLAB, to read at its source
mlx livescripts/FCD_Process_meanMAP.mlx, MATLAB, to read at its source
mlx livescripts/FCD_Process_surfMeanMap.mlx, MATLAB, to read at its source
mlx livescripts/FCD_Process_vox2cortex.mlx, MATLAB, to read at its source
mlx livescripts/Paper_inflatReg_Figs.mlx, MATLAB, to read at its source
mlx livescripts/SEEG_Process_AtlasLeft_surfer.mlx, MATLAB, to read at its source
mlx livescripts/SEEG_Process_AtlasLeft_surferFlat.mlx, MATLAB, to read at its source
mlx livescripts/SEEG_Process_AtlasRight_surfer.mlx, MATLAB, to read at its source
mlx livescripts/SEEG_Process_AtlasRight_surferFlat.mlx, MATLAB, to read at its source
mlx livescripts/SEEG_Process_Atlas_inflatVol.mlx, MATLAB, to read at its source
src/BIDS_batch_vox2cortex_v2.m, MATLAB, 161 lines
src/FCD_batch_inflatReg.m, MATLAB, 376 lines
src/FCD_batch_surfSquareReg_fine.m, MATLAB, 491 lines
src/MRI_func_GEO.mlx, MATLAB, to read at its source
src/MRI_func_ROI.m, MATLAB, 746 lines
src/MRI_func_brain.m, MATLAB, 169 lines, 1 match
src/MRI_read_Data.m, MATLAB, 656 lines
src/MRI_register.m, MATLAB, 304 lines
src/MRI_warp.m, MATLAB, 194 lines
src/Surf_func_ROI.m, MATLAB, 1,630 lines
src/Surf_voxelise.m, MATLAB, 1,267 lines
src/generate_feamaps.m, MATLAB, 71 lines
src/plot_mri.m, MATLAB, 625 lines
src/plot_surf.m, MATLAB, 376 lines
README.md, Text, 60 lines

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

docker/build.sh, Shell, 10 lines
preprocessing/abdomen/average_organ_template.py, Python, 98 lines
preprocessing/abdomen/compute_intensity_stats.ipynb, Jupyter, 66 lines
preprocessing/abdomen/preprocess_abdomen_ct_1k.ipynb, Jupyter, 249 lines
preprocessing/brain/preprocess.py, Python, 191 lines
preprocessing/brain/preprocess_scans.sh, Shell, 42 lines
preprocessing/brain/v2cc_resampling.sh, Shell, 55 lines
supplementary_material/abdomen_template/create_color_file.py, Python, 25 lines
supplementary_material/brain_template/create_color_file.py, Python, 18 lines
supplementary_material/brain_template/fsaverage/fs2ply.py, Python, 10 lines
supplementary_material/brain_template/fsaverage6/fs2ply.py, Python, 10 lines
vox2organ/__init__.py, Python, 10 lines
vox2organ/data/__init__.py, Python, 1 line
vox2organ/data/abdomen.py, Python, 201 lines
vox2organ/data/cortex.py, Python, 161 lines
vox2organ/data/cortex_labels.py, Python, 28 lines
vox2organ/data/dataset.py, Python, 311 lines
vox2organ/data/dataset_split_handler.py, Python, 21 lines
vox2organ/data/image_and_mesh_dataset.py, Python, 771 lines
vox2organ/data/supported_datasets.py, Python, 63 lines
vox2organ/logger/__init__.py, Python, 1 line
vox2organ/logger/logger.py, Python, 469 lines
vox2organ/main.py, Python, 298 lines
vox2organ/models/__init__.py, Python, 1 line
vox2organ/models/base_model.py, Python, 95 lines
vox2organ/models/graph_net.py, Python, 365 lines
vox2organ/models/model_handler.py, Python, 20 lines
vox2organ/models/u_net.py, Python, 322 lines
vox2organ/models/unet_flow.py, Python, 265 lines
vox2organ/models/vox2cortex.py, Python, 214 lines
vox2organ/params/__init__.py, Python, 1 line
vox2organ/params/default.py, Python, 361 lines
vox2organ/params/groups.py, Python, 154 lines
vox2organ/params/models.py, Python, 7 lines
vox2organ/utils/__init__.py, Python, 1 line
vox2organ/utils/coordinate_transform.py, Python, 188 lines
vox2organ/utils/cortical_thickness.py, Python, 105 lines
vox2organ/utils/custom_layers.py, Python, 15 lines
vox2organ/utils/distributed.py, Python, 90 lines
vox2organ/utils/eval_metrics.py, Python, 280 lines, 1 match
vox2organ/utils/evaluate.py, Python, 311 lines
vox2organ/utils/feature_aggregation.py, Python, 102 lines
vox2organ/utils/file_handle.py, Python, 81 lines
vox2organ/utils/graph_conv.py, Python, 200 lines
vox2organ/utils/losses.py, Python, 602 lines
vox2organ/utils/mesh.py, Python, 498 lines
vox2organ/utils/model_names.py, Python, 11 lines
vox2organ/utils/modes.py, Python, 22 lines
vox2organ/utils/template.py, Python, 287 lines
vox2organ/utils/test.py, Python, 251 lines
vox2organ/utils/train.py, Python, 886 lines
vox2organ/utils/train_test.py, Python, 17 lines
vox2organ/utils/utils.py, Python, 443 lines
vox2organ/utils/visualization.py, Python, 104 lines
LICENSE, License, 674 lines
README.md, Text, 140 lines

At the source: github.com/ai-med/Vox2Cortex

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

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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;
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Data

Datasets cited

openneuro:ds004199, at OpenNeuro; found in “Data and code availability”

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:

they point to a dataset: OpenNeuro ds004199
they point to the authors' code: ifsunrise/brain-inflate-registration
they say that the data are available on request
they say that the code is available on request

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

{ "id": "10.1016/j.isci.2026.117446", "type": "article-journal", "title": "Volume-inflation registration (INFREG) for morphometric analysis of human focal cortical dysplasia type II", "container-title": "iScience", "author": [ { "family": "Zhang", "given": "Yongsheng" }, { "family": "Han", "given": "Haojie" }, { "family": "Chen", "given": "Fang" }, { "family": "Liao", "given": "Hongen" } ], "container-title-short": "iScience", "volume": "29", "issue": "9", "page": "117446", "DOI": "10.1016/j.isci.2026.117446", "PMID": "42755994", "PMCID": "PMC13582014", "ISSN": "2589-0042", "publisher": "Elsevier", "URL": "https://doi.org/10.1016/j.isci.2026.117446", "language": "en", "issued": { "date-parts": [ [ 2026, 9, 9 ] ] } }

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