Contract no.

The project is co-financed by REACT-EU funding for mitigating the consequences of the pandemic crisis

The project aims to apply machine learning (ML) to microfluidics based on real-time data from reflected light microscopy, TEER (Trans Epithelial Electric Resistance) and O2 biosensors to grow different cell cultures (including those obtained from patient samples) on the OOC platform.

The project is implemented by EDI in cooperation with the Latvian Biomedical Research and Study Centre (BNC) and the Limited Liability Company “Cellboxlab”



During first reporting period, we have developed decision tree for data classification and produced first imaging data of successful and unsuccessful OOC cultivation data for developing AI models for supervising OOC cultivation. We identified possible approaches to the generation of synthetic data for training AI models. Due to the nature of the real-world data, the most promising approach is to generate synthetic data by means of generating simple geometric shapes and subsequently deforming them. We are currently conducting a survey of literature on that topic. We have investigated integrated objective/camera units for integration in the instrument from various providers with particular focus on evaluation of image quality, digital zoom capabilities and lighting conditions. We have started working on defining the procurement specification for XYZ gantry with a suitable XY step for continuous channel imaging and Z-step for successful autofocus on the aforementioned imaging units. Additionally, during this period, we engaged in public dissemination of project topic in student council of Riga Technical University organised online interview in Spiikiizi studio, titled “What if?”.


EDI investigated state-of-the-art approaches in literature to the generation of synthetic images of biological cells by deforming simple geometric shapes. Futhermore, EDI investigated the use of generative adversarial networks (GANs) for simulation-to-real transfer, which is needed to render synthetic images more realistic.


EDI researched deep neural network model architectures to find the best fit for the AimOOC task. We also looked for models pre-trained on medical images. A classification model was trained on the first data received from the partners, concluding that additional data is needed for a good result. Therefore, options for data augmentation that will allow for synthetic multiplication of training examples were also researched and summarized.

EDI researched methods of improving and multiplying training data. We studied methods included in the various Python libraries and frameworks, choosing and testing the ones best suited to the project images. EDI continued work on the generation of synthetic medical data, looking into the use of generative adversarial networks and novel diffusion models.

1.01.2023 – 31.03.2023.

After finishing work on the data augmentation method algorithm and the development of feature extraction algorithms, EDI continues work on the classification models, dividing the cell images according to time and cell lines. Models are trained according to the decision tree prepared in the earlier stages of the project. To improve model accuracy, we are currently working on two main tasks:

  1. images are trained by dividing them into smaller units – quadrilaterals without losing image details and increasing the number of images;
  2. work is being done on generating synthetic images with the help of Stable Diffusion to increase the amount of data and, thus, the accuracy of the classifier.

Participating scientists

    Mg. math. Laura Leja


    +371 67558147
    Maksims Ivanovs
    Mg. sc. cogn. Maksims Ivanovs


    +371 67558230
    Mg. math. Tamāra Laimiņa

    Research assistant

    +371 67558202; +371 67558207
    Dr. sc. ing. Roberts Kadiķis

    Senior Researcher

    +371 67558134