histomicstk

library1.4.0pypypiunverified

HistomicsTK is a Python toolkit for histopathology image analysis, providing algorithms for segmentation, feature extraction, and classification of whole-slide images. Current version is 1.4.0, with a release cadence of approximately 2-3 months.

ai-ml data

pip install histomicstk

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · HISTOMICSTK

histomicstk

ai-mlpythonv1.4.0

Install

—

Import

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Disk

—

Pass rate

0/ 10

Env Coverage0 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v? · pip install

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

build_error

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

histomicstk

✓ import histomicstk as htk

✗ import HistomicsTK

Case-sensitive; package is lowercase

segmentation.nuclear_segmentation

✓ from histomicstk.segmentation import nuclear_segmentation

✗ from histomicstk.segmentation.nuclei import nuclear_segmentation

Old path deprecated; use nuclear_segmentation submodule

features.compute_haralick_features

✓ from histomicstk.features import compute_haralick_features

✗ from histomicstk.features.haralick import compute_haralick_features

Haralick features moved to top-level features module in v1.4.0

Basic example of computing Haralick features from a 2D image. For whole-slide images, you need to install 'large_image' and use it to read tiles.

import histomicstk as htk import numpy as np # Load a sample image (replace with your own) # For whole-slide images, use large_image # Here we simulate a 2D grayscale image image = np.random.rand(100, 100).astype(np.float32) # Compute Haralick texture features from histomicstk.features import compute_haralick_features haralick = compute_haralick_features(image, return_labels=True) print(haralick['Harmonic mean']) # Nuclear segmentation (requires stain normalization first) from histomicstk.segmentation import nuclear_segmentation # nuclei_df, nuclei_im = nuclear_segmentation.nuclei_segmentation(image) # requires proper staining

histomics --version

Debug

Known issues

breakingIn v1.4.0, Python 3.8 support was dropped. You must use Python >=3.9.

fix

Upgrade Python to 3.9+ or pin histomicstk to <1.4.0.

affects: >=1.4.0

deprecatedDirect import of submodules like histomicstk.features.haralick is deprecated. Use histomicstk.features instead.

fix

Update imports to use top-level module: from histomicstk.features import compute_haralick_features

affects: >=1.4.0

gotchacompute_haralick_features expects float32 image in [0,1] range. Passing integer or unnormalized images yields incorrect results.

fix

Normalize image: image = image.astype(np.float32) / 255.0

affects: all

gotchaNuclear segmentation functions require stain normalization (e.g., Reinhard) before use. Skipping this step leads to poor segmentation.

fix

Use histomicstk.preprocessing.color_normalization.reinhard first.

affects: all

Upgrade

Version history

1.4.0latest on PyPI · released Feb 7, 2025

Audit

Dependencies

large_imageoptionalFor reading whole-slide images (openslide, DICOM, etc.)

numpyrequiredCore numerical computation

scikit-imagerequiredImage processing algorithms

pandasrequiredDataFrame operations for features

shapelyrequiredGeometric operations for annotations

Agent activity

10 hits · last 30 days

node

Resources

githubhttps://github.com/DigitalSlideArchive/HistomicsTK ↗

packagehttps://pypi.org/project/histomicstk/ ↗

homepagehttps://digitalslidearchive.github.io/HistomicsTK/ ↗