ImageXD presentation 30 March 2017. Developing software for biological image analysis using classic compute vision techniques and looking forward to deep learning segmentation and classification.

1. Developing high content image analysis
software for biologists
30 March 2017

2. Broad Institute
Use genomics to improve health
Study biology of disease and treatments
Collaborate and share knowledge

3. Imaging Platform
High content image and data analysis
Develop morphological profiling methods
Identify patterns and targets for disease

4. CellProfiler
Free and open source software
High throughput image analysis
Modular and reproducible

5. • Reproducible results
• Minimal computer vision knowledge
• Biology-focused documentation
• Modular and extensible
• Run locally or distributed
Good stuff

6. • Difficult to process bright-field data
• Support for proprietary microscope formats
• Pipeline configuration takes time
• 3D algorithms assume isotropic data
• Computationally expensive
• Results are “good enough”
Not-so-good stuff

7. • Address usability issues
What’s next?

10. • Address usability issues
• Transition from methods that generalize across
images to methods that generalize across
experiments, or even assays and microscopes.
What’s next?

11. • Address usability issues
• Transition from methods that generalize across
images to methods that generalize across
experiments, or even assays and microscopes.
• Transition from image segmentation (pixel labeling) to
semantic segmentation (scene labeling)
What’s next?

14. Thank you! 💖
cellprofiler.org
blog.cellprofiler.org
forum.cellprofiler.org
github.com/CellProfiler/CellProfiler
@CellProfiler on Twitter
cellprofiler.tumblr.com
github.com/imagi-ng
github.com/imagi-ng/research
github.com/imagi-ng/annotation
cytominer.org
github.com/cytominergallery
@clairemcquin