The intricate task of segmenting and quantifying cell nuclear pores in high-resolution 3D microscopy data is critical for cellular biology and disease research. This thesis introduces a deep learning pipeline crafted to automate the segmentation and quantification of nuclear pores from high-resolution 3D cell organelle images. Our aim is to refine computational methods capable of handling the data's complexity and size, thus improving accuracy and reducing manual labor in biological image analysis. The developed pipeline incorporates data preprocessing, augmentation strategies, random block sampling, and a three-stage post-processing algorithm. It utilizes a 3D U-Net with a VGG-16 backbone, optimized through cyclical data augmentation and random block sampling to tackle the challenges posed by limited labeled data and the processing of large-scale 3D images. The pipeline has demonstrated its capability to effectively learn and predict nuclear pore structures, achieving improvements in validation metrics compared to baseline models. Our experiments suggest that cyclical augmentation helps prevent overfitting, and random block sampling contributes to managing data imbalance. The post-processing phase successfully automates the quantification of nuclear pores without the need for manual intervention. The proposed pipeline offers an efficient and scalable approach to segmenting and quantifying nuclear pores in 3D microscopy images. Despite the ongoing challenges of computational intensity and data volume, the techniques developed in this study provide insights into the automation of complex biological image analysis tasks, with potential applications extending beyond the detection of nuclear pores. / Master of Science / This thesis outlines a computer program developed to automatically segment and count nuclear pores in 3D cell images, aiding cell and disease research. This program aims to handle large, complex image data more effectively, boost accuracy, and cut down the need for manual labor. We created a system that prepares data, applies a technique called augmentation to enrich it, selects specific image sections, and carries out a three-step final analysis. At the core of our program is a 3D U-Net model, a type of deep learning network, that has been enhanced to address the challenges of scarce labeled data and the processing of very large images. The system developed is capable of learning and identifying the structure of nuclear pores in cell images. Our experiments indicate that using augmentation in a cyclical manner during training can prevent overfitting, which is when a model learns the training data too well, and cannot suitably generalize. Selecting certain parts of the images for processing proves helpful with imbalanced data. Additionally, the program can automatically count nuclear pores in the final step. The proposed program is effective for analyzing and counting nuclear pores in 3D cell images and has the potential for broader applications in cell analysis. Despite the challenges of managing large datasets and the significant computational power required, our methods open new possibilities for automating cell studies, with uses extending beyond just nuclear pores.