Engineered heart tissues (EHTs) provide a promising platform for modeling cardiac physiology, but their dense and heterogeneous fiber organization makes quantitative analysis highly challenging. This thesis presents an automated pipeline for fiber segmentation and structural analysis of confocal EHT images. The framework integrates frequency based preprocessing using FFT bandpass filtering, state of the art deep learning segmentation models (U-Net, Attention U-Net, and U-Net++), and post-processing refinement through a secondary U-Net. Evaluation was conducted on a synthetic labeled dataset and on real EHT slices with sparse annotations. The results highlight clear trade-offs between model architectures. U-Net produced the most complete and connected fibers but introduced substantial hallucinations. Attention U-Net generated clean outputs but with fragmented fibers, and U-Net++ achieved a balance by capturing directionality and coherence with reduced continuity. Refinement networks were effective at reducing thickness and noise in some cases, but they often removed true fibers and fragmented long structures, providing limited overall benefit. Fiber level metrics and human inspection confirmed these findings, showing that orientation is captured reliably across models, while continuity and connectivity remain major challenges. Overall, the pipeline demonstrates the feasibility of automated structural analysis of EHTs and establishes a foundation for future work with improved datasets, advanced refinement strategies, and broader use of pretrained or structurally informed models. ... Read more

Accurate segmentation of anatomical structures and abnormalities in medical images is crucial, but manual segmentation is time-consuming and automated approaches lack clinical accuracy. In recent years, active learning approaches that aim to combine automatic segmentation with manual input have gained attention in the field, aiming to reduce the annotation effort required for training segmentation models. Batch query retrieval is a key component of active learning as it is a technique that allows for the simultaneous selection of multiple regions/points for annotation. This study investigates the effectiveness of discrete batch query retrieval methods compared to the traditional approach using gradient descent in the context of 3D medical image segmentation. Our experiments show that the active learning paradigm with batch query retrieval provides a few advantages over the gradient descent approach. Additionally, we analyze the impact of discretizing the query retrieval strategy on system performance. Our findings suggest that discretization can lead to slight performance degradation in terms of segmentation quality but offer computational advantages and faster convergence. We also discuss open issues, such as the interpretability of active learning methods, and recommend further research on combining active learning with other segmentation techniques. Overall, our study contributes to the understanding of active learning in medical image segmentation and provides insights for developing more efficient and accurate interactive segmentation models. ... Read more