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Bio-image Analysis Notebooks
Bio-image Analysis Notebooks
Trailer: Bio-image Analysis with Python
Setting up your computer
Python basics
Python code in Jupyter notebooks
Basic math in python
Pitfalls when working with Jupyter notebooks
Basic types in python
Lists and tuples
Cropping lists
Sorting lists
Masking numpy arrays
Dictionaries
Conditions
Loops
Functions
Importing functions and packages
Advanced python programming
Custom libraries
Functional parameters
Partial functions
Workflows in napari
Parallelization
Image analysis basics
Introduction to image processing
Working with images
Cropping images
Multi-channel image data
Image file formats
Machine learning basics
Supervised machine learning
Unsupervised machine learning
Scaling
GPU accelerated image processing
clEsperanto
Why GPU-acceleration makes sense
GPU-accelerated image processing using CUPY and CUCIM
Tracing memory consumption
Further reading
Image visualization in 3D
Multidimensional image stacks
Inspecting 3D image data with pyclesperanto
Interactive image visualization with napari
Image filtering
Filtering images
Convolution
Noise removal filters
Background removal filters
Edge detection
Processing images using SimpleITK
Image deconvolution
An introduction to image deconvolution
Determining the point-spread-function from a bead image by averaging
Richardson-Lucy-Deconvolution on OpenCL-compatible GPUs
Spatial transforms
Coordinate systems
Slicing and cropping
Affine transforms using scikit-image
Affine transforms using Scipy
Affine transforms using cupy
Affine transforms using clesperanto
Image segmentation
Image segmentation
Thresholding
Binary mask refinement
Split touching objects
Label images
Gauss-Otsu-labeling
Touching objects labeling
Voronoi-Otsu-labeling
Label image refinement
Remove labels on image edges
Sequential object (re-)labeling
Seeded watershed for membrane-based cell segmentation
Inner and outer cell borders
Post-processing for membrane-based cell segmentation
3D Image Segmentation
Machine learning for image segmentation
Pixel classification using Scikit-learn
Object segmentation on OpenCL-compatible GPUs
Pixel classification on OpenCL-compatible GPUs
Pixel classification in multi-channel images
Probability maps
Training pixel classifiers from folders of images
Generating feature stacks
Selecting features
Comparing segmentation algorithms
Scenario: Comparing different implementations of the same thresholding algorithm
Visual labeling comparison
Quantitative labeling comparison
Feature extraction
Quantitative image analysis
Counting bright objects in images
Basic statistics with pyclesperanto
Statistics using SimpleITK
Comparison of measurements from different libraries
Measuring intensity on label borders
Shape descriptors based on neighborhood graphs
Measuring distances between objects
Neighborhood analysis in tissues
Neighborhood definitions
Count touching neighbors
Draw distance-meshes between neighbors
Measure the distance to cells in another label image
Count proximal labels in an other label image
Neighbor meshes in three dimensions
Label neighbor filters
Cell classification
Object classification on OpenCL-compatible GPUs
Random forest decision making statistics
Object classification with scikit-learn
Object classification with APOC and SimpleITK-based features
Colocalization
Counting nuclei according to expression in multiple channels
Differentiating nuclei according to signal intensity
Graphical user interfaces
Interactive image visualization with napari
Interactive parameter tuning with napari and magicgui
Visualizing region properties in napari
Tribolium embryo morphometry over time in Napari
Interactive cropping with napari
Tiled image processing
Tiled image processing, a quick run-through
Tiling images - the naive approach
Tiling images with overlap
Connected component labeling in tiles
Measurements in objects in tiled images
Tiled image file formats: zarr
Map area of objects in tiles
Counting nuclei in tiles
Batch processing
How process files in a folder
Processing timelapse data
Tabular data wrangling
Introduction to working with DataFrames
Exploring tabular data
Selecting columns
Handling NaN values
Pivot tables
Descriptive statistics
Descriptive statistics
Descriptive statistics of labeled images
Method comparison
Plotting
Plotting using seaborn
Plotting with Matplotlib
Bland-Altman plots
Data visualization
Overlay texts on images
Parametric maps
Quantitative maps from neighbor statistics
Glossary
Imprint
repository
open issue
.md
.pdf
Colocalization
Colocalization
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