Quantitative Imaging in 3D

The qim3d (kɪm θriː diː) library is designed to make it easier to work with 3D imaging data in Python. It offers a range of features, including data loading and manipulation, image processing and filtering, visualization of 3D data, and analysis of imaging results.

You can easily load and process 3D image data from various file formats, apply filters and transformations to the data, visualize the results using interactive plots and 3D rendering, and perform quantitative analysis on the images.

Documentation available at https://docs.qim.dk/qim3d/

For more information on the QIM center visit https://qim.dk/

Installation

We recommend using a conda environment:

conda create -n qim3d python=3.11

After the environment is created, activate it by running:

conda activate qim3d

And then installation is easy using pip:

pip install qim3d

Remember that the environment needs to be activated each time you use qim3d!

For more detailed instructions and troubleshooting, please refer to the documentation.

Examples

Interactive volume slicer

import qim3d

vol = qim3d.examples.bone_128x128x128
qim3d.viz.slicer(vol)

viz slicer

Line profile

import qim3d

vol = qim3d.examples.bone_128x128x128
qim3d.viz.line_profile(vol)

line profile

Threshold exploration

import qim3d

# Load a sample volume
vol = qim3d.examples.bone_128x128x128

# Visualize interactive thresholding
qim3d.viz.threshold(vol)

threshold exploration

Synthetic data generation

import qim3d

# Generate synthetic collection of volumes
num_volumes = 15
volume_collection, labels = qim3d.generate.volume_collection(num_volumes = num_volumes)

# Visualize the collection
qim3d.viz.volumetric(volume_collection)

synthetic collection

Structure tensor analysis

import qim3d

vol = qim3d.examples.fibers_150x150x150
val, vec = qim3d.processing.structure_tensor(vol, visualize = True, axis = 1)

structure tensor

Support

The development of the qim3d is supported by the Infrastructure for Quantitative AI-based Tomography QUAITOM which is supported by a Novo Nordisk Foundation Data Science Programme grant (Grant number NNF21OC0069766).

Metadata

Release files for qim3d 1.5.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for qim3d 1.5.3
File	Size	Uploaded
qim3d-1.5.3.tar.gz	12.3 MB	Apr 11, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for qim3d 1.5.3
File	Interpreter	ABI	Platform	Reset
qim3d-1.5.3-py3-none-any.whl 12.4 MB Apr 11, 2026	Python 3	none	any	Details

Total release size: 24.8 MB

Release files / qim3d-1.5.3.tar.gz

Download URL	qim3d-1.5.3.tar.gz
Size	12.3 MB
Tags	Source
SHA-256 checksum How to use checksums	`9289eae531d679bbf2d324dfdae0ad1b04a317ad7ed13e28c626d00651d900b9`
BLAKE2b-256 checksum How to use checksums	`fd57bc1828bf90c14f8a7bbd8c9500555cd98689a2bc6e01037a198489368d93`
Upload date	Apr 11, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.11.15`

Release files / qim3d-1.5.3-py3-none-any.whl

Download URL	qim3d-1.5.3-py3-none-any.whl
Size	12.4 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`8fb1fab56af679e4bec14b41c9599bf84a6d9710c93b75a916f8a3dc54df789b`
BLAKE2b-256 checksum How to use checksums	`8bba2b68f8a2e7353ab5377aa1d4b10718e693c41a6004f6a0769783781c24c6`
Upload date	Apr 11, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.11.15`