Anastasija Zujeva
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Anastasija Zujeva has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Arnis Strods
Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia
1 shared publication - Arturs Abols
Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia
1 shared publication - Felikss Rumnieks
Latvian Biomedical Research and Study Centre
1 shared publication - Gatis Mozolevskis
Institute of Solid-State Physics, University of Latvia, 8 Kengaraga Str., LV-1063 Riga, Latvia
1 shared publication - Karīna Narbute
Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia
1 shared publication - Kārlis Gustavs Zviedris
Institute of Electronics and Computer Science (EDI), LV-1006 Riga, Latvia
1 shared publication
Latest Publications
- Open Access
Organ-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation
Organ-on-a-chip (OOC) technology has emerged as a groundbreaking approach for emulating the physiological environment, revolutionizing biomedical research, drug development, and personalized medicine. OOC platforms offer more physiologically relevant microenvironments, enabling real-time monitoring of tissue, to develop functional tissue models. Imaging methods are the most common approach for daily monitoring of tissue development. Image-based machine learning serves as a valuable tool for enhancing and monitoring OOC models in real-time. This involves the classification of images generated through microscopy contributing to the refinement of model performance. This paper presents an image dataset, containing cell images generated from OOC setup with different cell types. There are 3072 images generated by an automated brightfield microscopy setup. For some images, parameters such as cell type, seeding density, time after seeding and flow rate are provided. These parameters along with predefined criteria can contribute to the evaluation of image quality and identification of potential artifacts. This dataset can be used as a basis for training machine learning classifiers for automated data analysis generated from an OOC setup providing more reliable tissue models, automated decision-making processes within the OOC framework and efficient research in the future.
Open AccessOrgan-On-A-Chip (OOC) Image Dataset for Machine Learning and Tissue Model Evaluation
Organ-on-a-chip (OOC) technology has emerged as a groundbreaking approach for emulating the physiological environment, revolutionizing biomedical research, drug development, and personalized medicine. OOC platforms offer more physiologically relevant microenvironments, enabling real-time monitoring of tissue, to develop functional tissue models. Imaging methods are the most common approach for daily monitoring of tissue development. Image-based machine learning serves as a valuable tool for enhancing and monitoring OOC models in real-time. This involves the classification of images generated through microscopy contributing to the refinement of model performance. This paper presents an image dataset, containing cell images generated from OOC setup with different cell types. There are 3072 images generated by an automated brightfield microscopy setup. For some images, parameters such as cell type, seeding density, time after seeding and flow rate are provided. These parameters along with predefined criteria can contribute to the evaluation of image quality and identification of potential artifacts. This dataset can be used as a basis for training machine learning classifiers for automated data analysis generated from an OOC setup providing more reliable tissue models, automated decision-making processes within the OOC framework and efficient research in the future.