Exploring Metadata in Scientific Images

Tips Tricks 54 : Exploring Metadata in Scientific Images
What is Metadata?
It is the additional information about a file, providing details such as the
creation date, author, location, pixel size, experimental settings, etc.
Why is Metadata important?
For your travel images, it is important, so you know when and where the image was taken. Please note that metadata is not necessary but useful when some information is needed at a future date. May be your grandkids want to take a picture in future at the same location on your 100th birthday!
For scientific images, it is important to ensure traceability, interoperability,
and reproducibility.
Metadata provides a detailed history of the image, including acquisition parameters, equipment settings, and processing steps. This traceability is crucial for tracking the origin and evolution of the data, ensuring accountability and transparency in scientific research.
Standardized metadata formats enable different software and systems to understand and interpret information consistently. This promotes interoperability, allowing researchers to share and collaborate on data across various platforms and tools without losing critical details.
Metadata contains essential information about the experimental setup and conditions. Reproducing scientific experiments requires accurate knowledge of these factors. With comprehensive metadata, other researchers can precisely replicate experiments, verify results, and build upon existing work, contributing to the reliability and credibility of scientific findings.
Images come in many formats, let us explore metadata from a few most-common image formats including JPG, DICOM, TIFF, GEO-TIFF, OME-TIFF, and .CZI
Code from this video can be downloaded from here: github.com/bnsreenu/python_fo...
Useful links:
DICOM metadata tags: www.dicomlibrary.com/dicom/di...
TIFF tags: www.loc.gov/preservation/digi...
Open microscopy standard: docs.openmicroscopy.org/ome-m...

[@BHome-lz1zl]

Thanks for everything you keep sharing so far. I joined your channel while I had a project for Honey-pollen analysis and classifications.

[@Cammpopp]

Hello, i am working on a final year project on instance segmentation for plant disease and i found your video "Fine tuning Detectron2 for instance segmentation" very useful. I however need to calculate the area of an instance object or objects out of the entire image. Using your earlier video as an example, i wish to calculate the percentage of area occupied by the Alpha Granule on a single Cell image so that i can display that Alpha Granule occupy 5% of the Cell image. I will be glade if you can assist me on this or point me to further references that can help.

[@andrecolliard8139]

Hi @Cammpopp , if you were able to segment the region of interest and you got the segmentation map, you can calculate the area using regionprops from the sci-kit image library or calculate it by counting the pixels that belongs to that object. Once you now that, you can calculate the ratio by dividing the ROI area through the total area.

[@leibaleibovich5806]

I would like to ask you for an advice, if I can. I have a couple of decade-old videos (talk shows), which are rather small and blurry. I learned about this thing called "upscaling" and decided to give it a try. Essentially, I took a 10 sec video fragment from video, extracted frames (i.e. images) and passed them through the upscaler (if memory serves, ESRGAN). The end result was not particularly satisfying, but for some reason I got interested. I understood that there is no "magic button" and I need to learn what I am doing, if I want better results. Then I found your channel, which probably has the best explanation on image analysis in Python. The difficulty is that I do not know where to start and what I need to learn. As I said, I am interested in image upscaling. I would like to understand this technology just enough, so I can start experimenting on my own. I desperately need a "roadmap" for my learning. What would be the best learning map for this? Your advice will be appreciated. Thank you for your tutorials, Sreeni!

[@shubha1Ana2]

Hello Sreeni, very much informative your videos are. I am struggling to find HER2 positive WSIs fro TCGA. Can you just give some info

[@DigitalSreeni]

Not sure how I can help here. I do not have access to any WSI or any experience with the cancer genome atlas. Hopefully someone else with the right experience can help.

[@shubha1Ana2]

@@DigitalSreeni Thanks

[@scientificrunning7569]

Sir nenu mee videos regular ga follow avuthanu I am a professor actually but a student in deep learning and python chala proud ga vundi sir I shared your videos to all my students colleagues and relatives Ayurarogya prapithirastu sir meeku God bless you. Mee nunchi direct ga training thisuko vacha sir plz inform if you can

[@DigitalSreeni]

I wish I had time for direct training. నా ఆఫీస్ పని కొంచం ఎక్కువగానే ఉంటుంది. నా ఆఫీస్ పని కొంచం ఎక్కువగానే ఉంటుంది. Plus, on the weekends, ఫామిలీ టైం అండ్ ఫ్యూచర్ వీడియోస్ కి రీసెర్చ్ తో టైం అయిపోతుంది.

[@msuliman4296]

@@DigitalSreeni Sir please provide all the datasets which are use in this python Tutorial

[@msuliman4296]

@@DigitalSreeni please sir 🥲🥲