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.

The filtered-x LMS (FxLMS) algorithm has been widely used in active noise control (ANC) systems, where the secondary path is usually estimated online by injecting auxiliary noises. In such an ANC system, the ANC controller and the secondary-path estimator are coupled with each other, which make it difficult to analyze the performance of the entire system. Therefore, a comprehensive performance analysis of broadband ANC systems is not available currently to our best knowledge. In this paper, the convergence behavior of the FxLMS algorithm in broadband ANC systems with online secondary-path modeling is studied. Difference equations which describe the mean and mean square convergence behaviors of the adaptive algorithms are derived. Using these difference equations, the stability of the system is analyzed. Finally, the coupled equations at the steady state are solved to obtain the steady-state excess mean square errors (EMSEs) for the ANC controller and the secondary-path estimator. Computer simulations are conducted to verify the agreement between the simulated and theoretically predicted results. Moreover, using the proposed theoretical analysis, a systematic and simple design procedure for ANC systems is proposed. The usefulness of the theoretical results and design procedure is demonstrated by means of a design example.