An Optic Nerve Segmentation Model Based on Fully-Convolutional-Based Masked Autoencoders and Direction Field

Ultrasound measurement of optic nerve sheath diameter (ONSD) is considered a noninvasive method for estimating elevated intracranial pressure (ICP) in patients. Clinical trials have demonstrated a strong correlation between changes in ONSD and changes in ICP. Therefore, accurate segmentation of the ONSD is crucial for noninvasive ICP assessment. In this paper, we propose a two-stage self-supervised semantic segmentation method to enhance optic nerve segmentation. In the pre-training phase, we use a fully convolutional-based masked autoencoder (FCMAE) to reconstruct full images from partially masked inputs. The encoder of FCMAE aggregates contextual information to infer the masked image regions, and this pretrained encoder is then migrated to the segmentation task for parameter initialization. In the fine-tuning phase, we perform the optic nerve segmentation task. After obtaining the initial segmentation results through the UPerNet network, we use a direction field (DF) module to compute a vector of DFs pointing to the nearest edge of the optic nerve for each pixel. This DF information is then used to refine the initial segmentation results via the feature correction module. The model was trained on a dataset of optic nerve sheath images collected from hospital patients and achieved a Dice score of 98.03%. Our proposed method exhibits superior performance across all metrics compared to other segmentation models.