Preliminary program

Tuesday, 14 May 2024

08:30 – 09:00Registration and Installation checking (Fiji, Anaconda, Python environments&packages)
09:00 – 09:30Introduction into deep learning (DL) for image segmentation | Martin Čapek (IMG)
09:30 – 10:30StarDist segmentation block | Martin Čapek (IMG)
– Fiji practicals using 2D/3D images of various images and quality.
– Python practicals (Jupyter notebook) using a large image.
Coffee break
10:45 – 12:00Cellpose segmentation block | Jan Valečka (IMG)
– Fiji practicals using 2D/3D images, macro example.
– Cellpose standalone app practicals, retraining the model for new images.
12:00 – 12:30Omnipose bacteria segmentation block | Michaela Blažíková (IMG)
– Omnipose standalone app practicals using bacteria time-lapse data.
– Python practicals (Jupyter notebook).
13:15 – 14:15TrackMate block: tracking using DL segmentation models |
Michaela Blažíková (IMG)
– Short intro.
– Fiji practicals using time-lapse images.
14:15 – free available cloud platform for image processing including machine and deep learning | Pavel Krist (Zeiss)
Sponsor’s lecture
Coffee break
15:15 – 16:30Google Colab block: cloud-based distant computing | Michaela Blažíková (IMG)
Delta2 – bacteria tracking package using DL segmentation/tracking models.
ZeroCostDL4Mic practicals demos to process microscopy data.
16:30 – 18:00MitoSegNet block: mitochondrial segmentation and morphology analysis |
Martin Čapek (IMG)

Demo of the full process of DL-based analysis including image annotations, model retraining and morphological analysis.

– MitoSegNet standalone app – mitochondrial segmentation.
– Labkit plugin in Fiji – annotations of new mitochondrial images.
– MitoSegNet standalone app – retraining the model by using the annotated images.
– MitoSegNet standalone app – statistical morphological analysis of new segmented images.
18:00 – Virtual Reality based image segmentation and measurement: a Toy Demo |
Pavel Krist (Zeiss)

Sponsor’s practicals