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- Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2022/68-marianne-bioimage.md
- Meetup Event: https://www.meetup.com/data-umbrella/events/288670925/
- Video: https://youtu.be/NqdhuU1fX5A
- GitHub repo:
- Transcriber: ? [needs a transcriber]
- scikit-image on GitHub: https://github.com/scikit-image/scikit-image
- scikit-image website: https://scikit-image.org/
- scikit-image forum: https://discuss.scientific-python.org/c/contributor/skimage
- Alex’s recent talk on scikit-image: https://www.youtube.com/watch?v=tPNUX5NxlVY
- Example: https://scikit-image.org/docs/dev/auto_examples/applications/plot_fluorescence_nuclear_envelope.html
- Data files: https://gitlab.com/scikit-image/data
- CZI scikit-image grant: https://chanzuckerberg.com/?s=scikit-image
- https://scikit-image.org/docs/0.19.x/auto_examples/applications/plot_fluorescence_nuclear_envelope.html
scikit-image is a well-established Python library boasting a wide collection of image processing algorithms. We show that it is well-suited for bioimage data analysis workflows by presenting a biological application from the literature. The dataset is a time sequence of microscopy images of human cells. The presentation is intended as a step-by-step, follow-along tutorial.
## Timestamps
00:00 Data Umbrella Introduction
03:38 Speaker Introduction - Marianne Corvellec
04:26 Let’s get started for the presentation
05:30 Introduction to scikit-image
06:20 Measure fluorescence intensity at the nuclear envelope
07:58 Prep work to follow along
08:46 Checking the system
09:03 Importing the Python libraries
10:40 Import imageio to manage data
12:15 Analyze the data
15:32 Segment the nucleus rim
18:21 Apply the segmented rim as a mask
18:44 Measure the total intensity
19:53 Process the entire image sequence
26:20 Visualize: Change in fluorescence intensity at nuclear envelope the plot for change
28:14 Q&A Section
28:37 Q&A - Call to compare the result
29:56 Q&A - Pre-processing
31:59 Q&A - Future perspective for scikit-image
34:20 Q&A - scikit-image in the ecosystem of image processing
37:51 Q&A - confusion between scikit-learn and scikit-image
39:52 Q&A - scikitimage branding
41:29 Q&A - Biomedical image grant
43:00 Q&A - Drift detector for a classification model
44:35 Thanksgiving
Marianne Corvellec is a core developer of scikit-image, where she specializes in applications of image processing to the life sciences and other scientific fields. Her technical interests include data science workflows, data visualization, and best practices from testing to documenting. She holds a PhD in statistical physics from Ecole Normale Supérieure de Lyon, France. Since 2013, she has been a regular speaker and contributor in the Python, Carpentries, and FLOSS communities.
- GitHub: https://github.com/mkcor
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