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.