pArt of Science
As researchers, when we explore something, we are passionate about sharing it in a way that would be received more easily.
We write articles; we do presentations, and visual materials: photography, microscopy images, basic sketches, detailed illustrations, 3D models, are often the best narrators of our research.
Visual communication tools are powerful ways to grab the attention of the audience and enhance the memorability of the subject. Similarly, throughout my research, I needed visual materials to tell my story.
pArt of Science
As researchers, when we explore something, we are passionate about sharing it in a way that would be received more easily.
We write articles; we do presentations, and visual materials: photography, microscopy images, basic sketches, detailed illustrations, 3D models, are often the best narrators of our research.
Visual communication tools are powerful ways to grab the attention of the audience and enhance the memorability of the subject. Similarly, throughout my research, I needed visual materials to tell my story.
Research Interests
Tissue engineering, biomaterials,
additive manufacturing, emulsion templating,
polymer synthesis, porous materials,
decellularisation,
scientific and medical illustration
Hello!
Are you working in the field of emulsion templating?
Do you find it challenging to quantify pore and window sizes from SEM images of your emulsion-templated matrices? This process can be time-consuming and prone to user bias.
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Good news!
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We have developed a deep learning model, Pore D2, that automates the quantification of morphological features, such as pore and window sizes, in open-porous scaffolds.
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Give it a try, and please let us know if you have any questions or suggestions!
​​developed by
Ä°layda Karaca
Chemical Biotechnology, MSc Student
Bioengineering, BSc
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About Ä°layda:
She completed her Bachelor's degree in Bioengineering at the Izmir Institute of Technology in August 2023 and is currently pursuing a Master's degree in Chemical Biotechnology at the Technical University of Munich.
During her second year as an undergraduate, she interned for a year at the Laboratory of Biomedical Micro and Nanosystems (LBMS), where she gained valuable experience in machine learning and deep learning methods, focusing on object detection methodologies.
In her third year, Ä°layda began working in the Baldemir Lab under supervision of Dr. Betül Aldemir Dikici, concentrating on biomaterials and tissue engineering. In this lab, she learned the fundamentals of cell culture and scaffold development using the emulsion templating method. She also applied her previous knowledge in AI to develop a fully automated deep learning system for the quantitative analysis of emulsion-templated scaffolds from scanning electron microscope (SEM) images. This project received funding support from the Scientific and Technological Research Institution of Turkey (TUBITAK).