Live Cell Imaging Analysis Using Machine Learning and Synthetic Food Image Generation

Live cell imaging is a method to optically investigate living cells using microscopy images. It plays an increasingly important role in biomedical research as well as drug development. In this thesis, we focus on label-free mammalian cell tracking and label-free abnormally shaped nuclei segmentation of microscopy images. We propose a method to use a precomputed velocity field to enhance cell tracking performance. Additionally, we propose an ensemble method, Weighted Mask Fusion (WMF), combining the results of multiple segmentation models with shape analysis, to improve the final nuclei segmentation mask. We also propose an edge-aware Mask RCNN and introduce a hybrid architecture, an ensemble of CNNs and Swin-Transformer Edge Mask R-CNNs (HER-CNN), to accurately segment irregularly shaped nuclei of microscopy images. Our experiments indicate that our proposed method outperforms other existing methods for cell tracking and abnormally shaped nuclei segmentation.

While image-based dietary assessment methods reduce the time and labor required for nutrient analysis, the major challenge with deep learning-based approaches is that the performance is heavily dependent on the quality of the datasets. Challenges with food data include suffering from high intra-class variance and class imbalance. In this thesis, we present an effective clustering-based training framework named ClusDiff for generating high-quality and representative food images. From experiments, we showcase our method’s effectiveness in enhancing food image generation. Additionally, we conduct a study on the utilization of synthetic food images to address the class imbalance issue in long-tailed food classification.