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li_2025_annotations

Convert manual pathology annotations from Li et al. 2025 (provided as SVG files overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium spaceranger output.

Overview

Each SVG contains hand-drawn polygons (colored by morphological class) over an embedded low-resolution H&E image.

ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack

This tool:

  1. Parses polygons from the SVG and classifies them by stroke/fill color (tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial scatter plot.

Installation

uv venv
source .venv/bin/activate
uv pip install -e .

Usage

download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots

Paper

Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data. npj Precis. Oncol.9, 310 (2025). https://doi.org/10.1038/s41698-025-01104-3

About

Convert Li et al 2025 SVG to CSV based annotations

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Readme
MIT license
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Custom properties

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var __re = new RegExp('^' + "github\\.com" + '
GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
About
Convert Li et al 2025 SVG to CSV based annotations
Resources
Readme
MIT license
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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try {
var __m = "*";
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GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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.gitignore
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LICENSE
LICENSE
README.md
README.md
pyproject.toml
pyproject.toml
Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
About
Convert Li et al 2025 SVG to CSV based annotations
Resources
Readme
MIT license
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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highlight(document.body);
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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.gitignore
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LICENSE
LICENSE
README.md
README.md
pyproject.toml
pyproject.toml
Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
About
Convert Li et al 2025 SVG to CSV based annotations
Resources
Readme
MIT license
Activity
Custom properties
Stars
0 stars
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0 watching
Forks
0 forks
Report repository
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Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
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if (clean !== a.href) a.href = clean;
});
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observer.observe(document.body, { childList: true, subtree: true });
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(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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src/li_2025_annotations
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
pyproject.toml
pyproject.toml
Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
About
Convert Li et al 2025 SVG to CSV based annotations
Resources
Readme
MIT license
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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9 Commits
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src/li_2025_annotations
src/li_2025_annotations
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
pyproject.toml
pyproject.toml
Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
About
Convert Li et al 2025 SVG to CSV based annotations
Resources
Readme
MIT license
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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BranchesTags
Latest commit
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9 Commits
Folders and files
NameName
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src/li_2025_annotations
src/li_2025_annotations
.gitignore
.gitignore
LICENSE
LICENSE
README.md
README.md
pyproject.toml
pyproject.toml
Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
About
Convert Li et al 2025 SVG to CSV based annotations
Resources
Readme
MIT license
Activity
Custom properties
Stars
0 stars
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0 watching
Forks
0 forks
Report repository
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Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
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filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
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filter: invert(1) hue-rotate(180deg) !important;
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filter: none !important;
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filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
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::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
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::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - GenomicsMachineLearning/li_2025_annotations: Convert Li et al 2025 SVG to CSV based annotations · GitHub
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src/li_2025_annotations
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README.md
pyproject.toml
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Repository files navigation
li_2025_annotations
Convert manual pathology annotations from Li et al. 2025 (provided as SVG files
overlaid on H&E images) into per-spot CSV annotations aligned with 10x Visium
spaceranger output.
Overview
Each SVG contains hand-drawn polygons (colored by morphological class) over an
embedded low-resolution H&E image.
ClassColor
tumorred
immuneyellow
DCISdark blue
blood_vessellight blue
necrosisblack
This tool:
  1. Parses polygons from the SVG and classifies them by stroke/fill color
(tumor, immune, DCIS, blood vessel, necrosis).
  2. Aligns Visium fullres spot coordinates into the SVG's coordinate space using
the embedded image bounds and the SVG's transform matrix.
  3. Assigns each spot a label based on which polygon it falls within, using a
priority order to resolve overlaps.
  4. Writes a per-sample CSV (Barcode,Morphological Annotation) and a spatial
scatter plot.
Installation
uv venv
source .venv/bin/activate
uv pip install -e .
Usage
download # fetches spaceranger outputs and SVG annotations
process # parses SVGs, assigns annotations, writes CSVs + plots
Paper
Li, T., Yang, Q., Acs, B. et al. Computational pathology annotation enhances
the resolution and interpretation of breast cancer spatial transcriptomics
data. npj Precis. Oncol.9, 310 (2025).
https://doi.org/10.1038/s41698-025-01104-3
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Convert Li et al 2025 SVG to CSV based annotations
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MIT license
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