Volumetric Data Analysis and Visualization with `qim3d`

Felipe Delestro

Senior Research Software Specialist @ QIM Center

Danmarks Tekniske Universitet (DTU)

fima@dtu.dk

slides.com/delestro/zag-workshop

QIM Center

Center for Quantification of Imaging Data from MAX IV

supported by

Rebecca
Engberg

Center Manager

Anders Bjorholm Dahl

Head of the QIM Center

Jon
Sporring

Deputy Head of the QIM Center

qim.dk/about

Volumetric data

≠

volumetric rendering

data is composed of voxels (3D pixels)

mesh rendering

data is made of vertices and faces

10x10x10
voxels

Z slices

X slices

Visualization example

Image analys is different between `2D` and `3D` images

Operations should be performed in 3D.
Most tutorials and examples focus on 2D images.
Dataset size could pose a challenge.
Data visualization is more complex.

open-source Python library, that focuses on 3D volumetric data

Getting started is easy:
pip install qim3d

Documentation at platform.qim.dk/qim3d/

`Data handling`

Volumetric data can be stored in a variety of file formats.

This can make the very first step of importing the data more complex than it should be

load and save

`Data handling`

Volumetric data can be stored in a variety of file formats.

This can make the very first step of importing the data more complex than it should be

load and save

qim3d.io.load and qim3d.io.save can handle the most common data formats:

Tiff (including file stacks)
HDF5
TXRM/TXM/XRM
NIfTI
PIL (including file stacks)
VOL/VGI
DICOM

import qim3d

vol = qim3d.io.load("path/to/image.tif", virtual_stack=True)

`Data handling`

We make a distinction between load/save and import/export when the operation includes transformations to the data.

import and export

Currently we're implementing a 3D import/export following the OME-Zarr standard, which
uses chunked data with a
ulti-scales scheme.

`Data handling`

We have a repository of 3D volumetric data that can be freely used, and with qim3d they can be easily downloaded directly within python code

Downloader

import qim3d

downloader = qim3d.io.Downloader()
data = downloader.Cowry_Shell.Cowry_DOWNSAMPLED(load_file=True)

`Data handling`

Generating synthetic volumetric data can be very helpful for method development, benchmarking or tutorials

Synthetic data

The function qim3d.generate.volume uses Perlin noise to generate a variety of complex volumes

vol = qim3d.generate.volume(noise_scale = 0.015)

vol = qim3d.generate.volume(base_shape = (200, 100, 100),
                        final_shape = (400, 100, 100),
                        noise_scale = 0.03,
                        gamma = 0.12,
                        threshold = 0.85,
                        object_shape = "tube"
                        )

`Data handling`

When using qim3d.generate.volume_collection we can generate groups of objects within the same volume, and also have their individual labels

import qim3d

# Generate synthetic collection of objects
num_objects = 15
vol, labels = qim3d.generate.volume_collection(num_objects = num_objects)

vol

labels

`Data handling`

vol, labels = qim3d.generate.volume_collection(num_objects = 40,
                                        collection_shape = (300, 150, 150),
                                        min_shape = (280, 10, 10),
                                        max_shape = (290, 15, 15),
                                        min_object_noise = 0.08,
                                        max_object_noise = 0.09,
                                        max_rotation_degrees = 5,
                                        min_threshold = 0.7,
                                        max_threshold = 0.9,
                                        min_gamma = 0.10,
                                        max_gamma = 0.11,
                                        object_shape = "cylinder"
                                        )

vol, labels = qim3d.generate.volume_collection(num_objects = 10,
                                        collection_shape = (200, 200, 200),
                                        min_shape = (180, 25, 25),
                                        max_shape = (190, 35, 35),
                                        min_object_noise = 0.02,
                                        max_object_noise = 0.03,
                                        max_rotation_degrees = 5,
                                        min_threshold = 0.7,
                                        max_threshold = 0.9,
                                        min_gamma = 0.10,
                                        max_gamma = 0.11,
                                        object_shape = "tube"
                                        )

`Visualization`

Easily exploring volumetric data may not be so straightforward. The library provides helper functions this task simple and easy, even within Jupyter Notebooks

import qim3d

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

`Visualization`

import qim3d

vol = qim3d.examples.shell_225x128x128
qim3d.viz.slices_grid(vol, num_slices=15)

`Visualization`

import qim3d

vol = qim3d.examples.cement_128x128x128
binary = qim3d.filters.gaussian(vol, sigma = 2) < 60
labeled_volume, num_labels = qim3d.segmentation.watershed(binary)

color_map = qim3d.viz.colormaps.segmentation(num_labels, style = 'bright')
qim3d.viz.slicer(labeled_volume, slice_axis = 1, color_map=color_map)

qim3d.viz.colormaps.segmentation creates an colormap designes specifically for segmentation tasks

The parameter min_dist allows us to control how different neighbour colors are

`Visualization`

import qim3d

# Generate synthetic collection of objects
num_objects = 15
vol, labels = qim3d.generate.volume_collection(num_objects = num_objects)

# Visualize synthetic collection
qim3d.viz.volumetric(vol)

`Visualization`

import qim3d

vol = qim3d.generate.volume(base_shape=(128,128,128),
                          final_shape=(128,128,128),
                          noise_scale=0.03,
                          order=1,
                          gamma=1,
                          max_value=255,
                          threshold=0.5,
                          dtype='uint8'
                          )

mesh = qim3d.mesh.from_volume(vol, step_size=3)

qim3d.viz.mesh(mesh.vertices, mesh.faces)

`Visualization`

Visualization for OME-Zarr datasets is more challenging, but very important for large volumes

The qim3d library integrates with the itk-vtk-viewer, that can be launched locally from the command line

`Visualization`

Exploring chunks from a OME-Zarr dataset is also possible directly on a Jupyter notebook

import qim3d

# Download dataset
downloader = qim3d.io.Downloader()
data = downloader.Snail.Escargot(load_file=True)

# Export as OME-Zarr
qim3d.io.export_ome_zarr("Escargot.zarr", data, chunk_size=100, downsample_rate=2, replace=True)

# Explore chunks
qim3d.viz.chunks("Escargot.zarr")

`Image analysis`

The goal of qim3d is to cover form basic functionalities that can serve as building blocks for more complex pipelines, up to implementation or wrappers for more complex methods

This will allow the library to be a central point for building pipelines for processing volumetric data

`Image analysis`

qim3d.filters contains implementations for common filter operations, including the option to run them on chunked data using Dask

qim3d.filters.Pipeline allows the creation of classes that act as filter pipelines

import qim3d
from qim3d.filters import Pipeline, Median, Gaussian, Maximum, Minimum

# Get data
vol = qim3d.examples.fly_150x256x256

# Show original
fig1 = qim3d.viz.slices_grid(vol, num_slices=5, display_figure=True)

# Create filter pipeline
pipeline = Pipeline(
    Median(size=5),
    Gaussian(sigma=3, dask = True)
)

# Append a third filter to the pipeline
pipeline.append(Maximum(size=3))

# Apply filter pipeline
vol_filtered = pipeline(vol)

# Show filtered
fig2 = qim3d.viz.slices_grid(vol_filtered, num_slices=5, display_figure=True)

`Image analysis`

The modules qim3d.detection and qim3d.segmentation contain wrappers for common methods that are useful when building larger pipelines

import qim3d

# Get data
vol = qim3d.examples.cement_128x128x128
vol_blurred = qim3d.filters.gaussian(vol, sigma=2)

# Detect blobs, and get binary_volume
blobs, binary_volume = qim3d.detection.blobs(
    vol_blurred,
    min_sigma=1,
    max_sigma=8,
    threshold=0.001,
    overlap=0.1,
    background="bright"
    )

# Visualize detected blobs
qim3d.viz.circles(blobs, vol, alpha=0.8, color='blue')

`Image analysis`

The modules qim3d.detection and qim3d.segmentation contain wrappers for common methods that are useful when building larger pipelines

import qim3d

vol = qim3d.examples.cement_128x128x128
bin_vol = qim3d.filters.gaussian(vol, sigma = 2)<60

fig1 = qim3d.viz.slices_grid(bin_vol, slice_axis=1, display_figure=True)

labeled_volume, num_labels = qim3d.segmentation.watershed(bin_vol)

cmap = qim3d.viz.colormaps.segmentation(num_labels)
fig2 = qim3d.viz.slices_grid(labeled_volume, slice_axis=1, color_map=cmap, display_figure=True)

`Image analysis`

qim3d.operations gathers image analysis operations that perform more specific tasks. For example, here we use fade_mask to remove artifacts form the edge of a dataset

import qim3d
vol = qim3d.io.load('heartScan.tif')
qim3d.viz.volumetric(vol)

vol_faded = qim3d.operations.fade_mask(vol, decay_rate=4, ratio=0.45, geometric='cylindrical')
qim3d.viz.volumetrics(vol_faded)

`Image analysis`

qim3d.processing is meant for more complex methods, that perform quantification tasks that are specific but common for volumetric data

The current implementations are:

qim3d.processing.structure_tensor
qim3d.processing.local_thickness

`Image analysis`

Structure tensor

import qim3d

vol = qim3d.examples.NT_128x128x128
val, vec = qim3d.processing.structure_tensor(vol, visualize=True, axis=2)

`Image analysis`

Local thickness

import qim3d

vol = qim3d.examples.fly_150x256x256
lt_vol = qim3d.processing.local_thickness(vol, visualize=True, axis=0)

`Machine learning`

This module is still in the early stages of development, but our goal is to have a set of tools that can streamline the implementation of ML & DL pipelines for volumetric data

For example, currently, there are a few helper functions for setting DL archtechtures

# Make an Unet 
my_model = qim3d.ml.models.UNet(size = 'medium', dropout = 0.25)

# Set augmentations
my_aug = qim3d.ml.Augmentation(resize = 'crop', transform_train = 'light')

# Set hyperparameters
my_hyperparameters = qim3d.ml.Hyperparameters(my_model, n_epochs=5, learning_rate = 5e-3, 
                                              loss_function='DiceCE',weight_decay=1e-3)

# training model
qim3d.ml.train_model(my_model, my_hyperparameters, train_loader, val_loader, plot=True)

`Command line interface`

fima@pop-os ~ $ qim3d viz Okinawa_Foram_1.zarr/


itk-vtk-viewer
=> Serving /home/fima/Notebooks/Qim3d on port 3000

    enp0s31f6 => http://10.52.0.158:3000/
    wlp0s20f3 => http://10.197.104.229:3000/

Serving directory '/home/fima/Notebooks/Qim3d'
http://localhost:8042/

Visualization url:
http://localhost:3000/?rotate=false&fileToLoad=http://localhost:8042/Okinawa_Foram_1.zarr

The library is an open-source project, and you're all welcome to contribute!

github.com/qim-center/qim3d

documentation and
installation instructions:

platform.qim.dk/qim3d

Large datasets
and the
`OME-Zarr`
standard

16Gb

a commom amount of memory for a computer

512Gb

memory of a good HPC node

1000Gb (1Tb)

Data size that we're supposed to handle

Chunked storage: Data is divided into chunks, allowing efficient access and processing of subsets.
Compression support: Chunks can be compressed using various algorithms (e.g., Blosc, Zstd, Gzip) to reduce storage requirements.
Scalability: Works well with large datasets, enabling parallel I/O and distributed computing.
Efficient random access: Since data is chunked, specific regions can be read or written without loading the entire dataset.

Interoperability: Ensures compatibility across imaging software, hardware, and analysis tools.
OME Data Model: Defines a standardized way to represent metadata (e.g., microscope settings, acquisition parameters) for reproducibility.
OME Files & Formats: Provides tools like Bio-Formats for reading and writing various imaging file formats.
OME-NGFF (Next-Generation File Format): A vision for scalable, cloud-native bioimaging data storage.

(Open Microscopy Environment)

specifications for imaging data

chunked file storage

lower resolution scales of the data

original high resolution data

dataset is divided into managable chunks

Practical session notes

conda create -n qim3d python=3.11
conda activate qim3d
pip install qim3d

test by running qim3d on the terminal, or pip show qim3d

pip install jupyter

Install jupyter

documentation for qim3d: platform.qim.dk/qim3d/

slides.com/delestro/qim3d

Data visualization for cultural heritage science

By Felipe Delestro

Data visualization for cultural heritage science

Volumetric Data Analysis and Visualization with qim3d

QIM Center

Rebecca Engberg

Anders Bjorholm Dahl

Jon Sporring

Volumetric data

Image analys is different between 2D and 3D images

Data handling

load and save

Data handling

Data handling

load and save

Data handling

import and export

Data handling

Downloader

Data handling

Synthetic data

Data handling

Data handling

Visualization

Visualization

Visualization

Visualization

Visualization

Visualization

Visualization

Visualization

Image analysis

Image analysis

Image analysis

Image analysis

Image analysis

Image analysis

Image analysis

Image analysis

Structure tensor

Image analysis

Local thickness

Machine learning

Machine learning

Command line interface

Command line interface

Large datasets and the OME-Zarr standard

16Gb

512Gb

1000Gb (1Tb)

Practical session notes

Data visualization for cultural heritage science

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Large datasets
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