Hlybovets, AndriyBrudno, MichaelKucheruk, Dariia2024-04-042024-04-042023https://ekmair.ukma.edu.ua/handle/123456789/28652This thesis proposes a multi-label segmentation model for optimizing the segmentation of neonatal brain MRI with partially annotated data. A multi-label segmentation model that addresses the challenges of limited annotated data by modifying the preprocessing, loss function, and postprocessing of the original multi-class label segmentation was developed. The proposed approach aims to improve the accuracy and efficiency of neonatal brain MRI segmentation by leveraging partially annotated data. We evaluate our method on a unique dataset of neonatal brain MRI and demonstrate its effectiveness compared to the models trained on fully annotated data.enautomatic Segmentation in the Field of Healthcareevaluation Metricsplace for improvementthe Nature of the Datasetbachelor's thesisOther