ABSTRACT
Intracranial hemorrhage (ICH) resulting from traumatic brain injury is a serious issue, often leading to death or long-term disability if not promptly diagnosed. Currently, doctors primarily use Computerized Tomography (CT) scans to detect and precisely locate a hemorrhage, typically interpreted by radiologists. However, this diagnostic process heavily relies on the expertise of medical professionals. To address potential errors, computer-aided diagnosis systems have been developed. In this study, we propose a new method that enhances the localization and segmentation of ICH lesions in CT scans by using multiple images created through different data augmentation techniques. We integrate residual connections into a U-Net-based segmentation network to improve the training efficiency. Our experiments, based on 82 CT scans from traumatic brain injury patients, validate the effectiveness of our approach, achieving an IOU score of 0.807 ± 0.03 for ICH segmentation using 10-fold cross-validation.
ABSTRACT
Minimally invasive glaucoma surgeries (MIGS) and non-penetrating glaucoma procedures have changed the surgical management of glaucoma in recent years and are being explored in the management of uveitic glaucoma. Deep sclerectomy, Xen45 gel stent, gonioscopy-assisted transluminal trabeculotomy (GATT), trabectome, and canaloplasty are described in the literature as alternative surgeries that may work for uveitic glaucoma. While most of the published studies are retrospective case series, the results suggest success even in refractory cases and with less inflammation than more traditional surgeries.