Explore computer vision methods for automated segmentation and analysis of subcellular components in cryo-electron tomography (cryoET) data. Delve into the challenges of analyzing 3D tomogram data and learn about approaches for segmenting mitochondrial volumes and granules in iPSC-derived neurons from Huntington's Disease patient samples. Discover ongoing research on label-efficient segmentation techniques to scale computer vision analysis to larger numbers of features. Examine the potential of unsupervised learning methods, including hyperbolic space models and domain adaptive region-based convolutional neural networks, for biomedical image segmentation. Gain insights into the future directions of computer vision applications in cryoET data analysis and their implications for understanding subcellular structures and interactions.