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To address this, we present a fully automated modular pipeline combining deep learning and semisupervised learning techniques for ONSD estimation from axial head CT scans. The pipeline features three stages: (1) deep learning\u2010based slice selection, (2) semisupervised machine learning for optic nerve segmentation utilizing both labeled and unlabeled publicly available data, and (3) geometric and morphological ONSD measurement. When validated on public and internal datasets, our approach generalized better than traditional supervised segmentation methods, achieving an intersection over union (IoU) of 0.561 \u00b1 0.141 and Dice coefficient of 0.707 \u00b1 0.131 on previously unseen data. Slice\u2010wise measurement accuracy varied based on measurement distance from the ocular globe, yielding mean absolute errors as low as 1.779 and 1.899\u2009mm for the right and left ONSD, respectively. 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