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Quantitative Bio-image Analysis with Python
Quantitative Bio-Image Analysis using Python
Course preparation
Day 1: Introduction to Python and Bio-image Analysis
Introduction to Python
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
Introduction to Bio-image Analysis
Opening and Displaying with Images in Python
Working with images
Subplots with matplotlib
Multi-channel image data
Image file formats
Folder Structures
Folder Structures
Folder Structures
napari from Jupyter notebooks
Using Napari to visualize and interact with images
Segmentation Workflow Example with 3D Data
Image Filters
Image Filters
3D Image Filters
Day 2: Image Filtering, Segmentation and Feature Extraction
Background subtraction
Removing image noise
Image segmentation
Thresholding
Morphological Image Processing
Otsu’s threshold method (optional)
Instance Segmentation
Label images
Touching objects labeling
Voronoi tesselation
Voronoi-Otsu-labeling
3D Image Segmentation
Seeded watershed for membrane-based cell segmentation
Remove labels on image edges
Label image refinement
The Segmentation Game
Image segmentation quality measurements
Machine learning for object segmentation
Supervised machine learning
Exercise: Interactive pixel and object classification
Object segmentation on OpenCL-compatible GPUs
Training classifiers from folders of images
Deep Learning for image segmentation
Stardist in Python: Data
Stardist in Python: Training
Training a 2D Unet model
Feature extraction
Feature extraction
Day 3: Biostatistics and data science
Working with tabular data
Introduction to working with DataFrames
Basic descriptive statistics
Descriptive statistics of labeled images
Appending tables
Handling NaN values
Tidy-Data
Split-Apply-Combine
Plotting Data with Seaborn
Plotting Data with Python
Introduction to Seaborn
Plotting Distributions with Seaborn
Multivariate views
Statistics and tests
Method comparison and Bland-Altman Analysis
Correlation
Correlation matrix
Multiple testing
Nonparametric testing
Dimensionality reduction
UMAP
UMAP
PCA (Principle Component analysis)
Clustering
K-means clustering
HDBSCAN
Interactive dimensioanlity reduction and clustering
Day 4: Best practices in scientific programming and developing Napari plugins
Best practices in scientific programming
Writing readable code
Writing good code
Prevent magic numbers
Divide and rule
Custom modules
Reading exercise
Exercise modularization
Keep it short and simple
Interactive functions in Jupyter Lab
Make your own napari plugin
Creating Widgets from Functions
Creating a plugin from a template using the cookiecutter
Day 5: Surfaces and bring your own data
Working with points and meshes
Working with surface data
Advanced surface characteristics
Work on your own data
repository
open issue
Index
