Image analysis in Biosciences

08.11.2024

Time of occurrence

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More information

Credits

1 ECTS (26 hours)

Form of study

Partially web-based

Location

Tartu

Fee

free

Teachers

Miriam Ann Hickey

Study group

Health and welfare n.e.c.

Program language

English

Purpose:

Introduction to automated and semi-automated image analysis for biosciences.
Students will be introduced to software including ImageJ, CellProfiler, QuPath, artificial-intelligence based analysis in NumPy and Tensorflow/PyTorch, and the creation of publication-ready images using Inkscape.

Target group:

Doctoral students (all years), Master's students, supervisors of doctoral students of all Estonian universities.

Additional information

Outcome:

Students will create a dataset using ImageJ, CellProfiler, QuPath, NumPy and Tensorflow/PyTorch and will create a publication-ready figure using InkScape

Teachers:

Miriam Ann Hickey - PhD

Content:

Topic	Credit	Lectors
1. Lecture CellProfiler: -Setting up the pipeline -Identifying objects -Filtering -Choice of outcome measure -Exporting images / masks -Exporting data	2	Miriam Ann Hickey
2. Lunch: Working lunch and discussion of the imaging hub (website)	1	Miriam Ann Hickey
3. AI for Medical Image Analysis: Creating a Medical Image Segmentation and Detection Project TensorFlow/ Pytorch-Lightning environment: -Data Collection -Model Training -Model Evaluation -Model Deployment	3	Miriam Ann Hickey
4. QuPath: -Creating a project -Creating annotations -Volume measurements -Neuronal counting (manual / automated)	2	Miriam Ann Hickey
5. Inkscape: -Preparation of figures for publication -Insertion / changing size of photomicrographs, graphs -Alignment -Scalebars -Text	2	Miriam Ann Hickey
6. Personal time	13	Miriam Ann Hickey
7. Lecture: ImageJ -Splitting / merging channels -Making Z stacks / 3D projections -Segmenting 2D and 3D (manual, automated) -Filtering -Counting / quantification (manual / automated) -RoI manager -Batch processing -Basic macros	3	Miriam Ann Hickey

Price info:

Free

Location info:

Tartu linn Biomeedikum, Ravila 19, 50411 Tartu, room 1006

Learning environment:

1-day course

Schedule and further information:

The lectures will take place on 8th of November 2024 from 9.30 to 17.45.

Requirements to complete:

Home assignment is due within 1 week of the 1-day intensive course. Submission of home assignment within course Moodle page. Students were present for the entire 8 hours of lectures.

Outcome method:

non-differentiated (pass, fail, not present)

Grading method:

Home work

Grading criteria:

Home assignment is due within 1 week of the 1-day intensive course. Submission of home assignment within course Moodle page (Pass-fail). Fail Publication-ready figure not submitted or publication-ready image is of very poor quality (e.g., no legend, very poor format) Pass Publication-ready figure is of sufficient quality (e.g., legend is sufficient for understanding of figure content, statistics are appropriately applied, images are of sufficient quality and are appropriate for the graphs, graphs are appropriately formatted)

Document to be issued:

Certificate of completion

Additional information:

Miriam Ann Hickey, miriam.ann.hickey@ut.ee, +372 +372 737 4361

Program code:

MVBS.TK.020

Apply in SIS

General contact:

Lifelong learning center

Ülikooli 18, Tartu, 50090

+372 737 6213

taiendusope@ut.ee