Optic nerve sheath diameter semantic segmentation and feature extraction

Abstract: Traumatic brain injury (TBI) affects millions of people worldwide, leading to significant mortality and disability rates. Elevated intracranial pressure (ICP) resulting from TBI can cause severe complications and requires early detection to improve patient outcomes. While invasive methods are commonly used to measure ICP accurately, non-invasive techniques such as optic nerve sheath diameter (ONSD) measurement show promise. This study aims at the creation of a tool that can automatically perform a segmentation of the ONS from a head computed tomography (CT) scan, and extracts meaningful measures from the segmentation mask, that can be used by radiologists and medics when treating people affected by TBI. This has been achieved using a deep learning model called ”nnU-Net”, commonly adopted for semantic segmentation in medical contexts. The project makes use of manually labeled head CT scans from a public dataset named CQ500, to train the aforementioned segmentation model, using an iterative approach. The initial training using 33 manually segmented samples demonstrated highly satisfactory segmentations, with good performance indicated by Dice scores. A subsequent training, combined with manual corrections of 44 unseen samples, further improved the segmentation quality. The segmentation masks enabled the development of an automatic tool to extract and straighten optic nerve volumes, facilitating the extraction of relevant measures. Correlation analysis with a binary label indicating potential raised ICP showed a stronger correlation when measurements were taken closer to the eyeball. Additionally, a comparison between manual and automated measures of optic nerve sheath diameter (ONSD), taken at a 3mm distance from the eyeball, revealed similarity between the two methods. Overall, this thesis lays the foundation for the creation of an automatic tool whose purpose is to make faster and more accurate diagnosis, by automatically segmenting the optic nerve and extracting useful prognostic predictors.