The evolution of software engineering is increasingly driven by the integration of artificial intelligence and digital paradigms. In critical domains such as medical and biological imaging, modern software systems are transitioning from static, rule-based workflows to dynamic, AI-powered environments. This paper presents the design, implementation, and preliminary validation of an advanced, fully automated software pipeline for Transmission Electron Microscopy image analysis to address software engineering and analytical challenges in tendon fibril segmentation. The proposed architecture replaces a previous hybrid segmentation approach with a deep learning model based on the ViT-UNet architecture, integrating targeted preprocessing modules, advanced segmentation capable of capturing both local details and global spatial relationships, and an automated feature extraction and clustering engine. Initial experimental results show an improvement in clustering accuracy from 57.14% to 64.71%. While these findings suggest the potential of Transformer-based architectures to enhance medical image analysis, they also highlight the need for further evaluation on larger datasets to establish broader statistical significance and validate their overall effectiveness.