Objective: To assess the rate of iatrogenic injury to the inner ear in vestibular schwannoma resections.
Study Design: Retrospective case review.
Setting: Multiple academic tertiary care hospitals.
Purpose: Drilling injuries of the inner ear are an underreported complication of lateral skull base (LSB) surgery. Inner ear breaches can cause hearing loss, vestibular dysfunction, and third window phenomenon. This study aims to elucidate primary factors causing iatrogenic inner ear dehiscences (IED) in 9 patients who presented to a tertiary care center with postoperative symptoms of IED following LSB surgery for vestibular schwannoma, endolymphatic sac tumor, Meniere's disease, paraganglioma jugulare, and vagal schwannoma.
View Article and Find Full Text PDFThe foreign body response to implanted materials often complicates the functionality of sensitive biomedical devices. For cochlear implants, this response can reduce device performance, battery life and preservation of residual acoustic hearing. As a permanent and passive solution to the foreign body response, this work investigates ultra-low-fouling poly(carboxybetaine methacrylate) (pCBMA) thin film hydrogels that are simultaneously photo-grafted and photo-polymerized onto polydimethylsiloxane (PDMS).
View Article and Find Full Text PDFObjective: To evaluate the historical descriptive origins of the extracranial transnasal transsphenoidal route to the sphenoid sinus and sella turcica focusing on the works of two otolaryngologists: Markus Hajek (1861-1941) and Oskar Hirsch (1877-1965).
Data Sources: A collection of primary references of author publications, and contemporary references and textbooks.
Review Methods: Primary references were reviewed with specific focus on surgical routes to the sphenoid sinus and sella turcica.
Comput Med Imaging Graph
April 2021
In medical image segmentation tasks, deep learning-based models usually require densely and precisely annotated datasets to train, which are time-consuming and expensive to prepare. One possible solution is to train with the mixed-supervised dataset, where only a part of data is densely annotated with segmentation map and the rest is annotated with some weak form, such as bounding box. In this paper, we propose a novel network architecture called Mixed-Supervised Dual-Network (MSDN), which consists of two separate networks for the segmentation and detection tasks respectively, and a series of connection modules between the layers of the two networks.
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