RESUMEN
Correct positioning of the endoscope is crucial for successful hip arthroscopy. Only with adequate alignment can the anatomical target area be visualized and the procedure be successfully performed. Conventionally, surgeons rely on anatomical landmarks such as bone structure, and on intraoperative X-ray imaging, to correctly place the surgical trocar and insert the endoscope to gain access to the surgical site. One factor complicating the placement is deformable soft tissue, as it can obscure important anatomical landmarks. In addition, the commonly used endoscopes with an angled camera complicate hand-eye coordination and, thus, navigation to the target area. Adjusting for an incorrectly positioned endoscope prolongs surgery time, requires a further incision and increases the radiation exposure as well as the risk of infection. In this work, we propose an augmented reality system to support endoscope placement during arthroscopy. Our method comprises the augmentation of a tracked endoscope with a virtual augmented frustum to indicate the reachable working volume. This is further combined with an in situ visualization of the patient anatomy to improve perception of the target area. For this purpose, we highlight the anatomy that is visible in the endoscopic camera frustum and use an automatic colorization method to improve spatial perception. Our system was implemented and visualized on a head-mounted display. The results of our user study indicate the benefit of the proposed system compared to baseline positioning without additional support, such as an increased alignment speed, improved positioning error and reduced mental effort. The proposed approach might aid in the positioning of an angled endoscope, and may result in better access to the surgical area, reduced surgery time, less patient trauma, and less X-ray exposure during surgery.
RESUMEN
Magnetic resonance imaging (MRI) is an important imaging modality in stroke. Computer based automated medical image processing is increasingly finding its way into clinical routine. The Ischemic Stroke Lesion Segmentation (ISLES) challenge is a continuous effort to develop and identify benchmark methods for acute and sub-acute ischemic stroke lesion segmentation. Here we introduce an expert-annotated, multicenter MRI dataset for segmentation of acute to subacute stroke lesions ( https://doi.org/10.5281/zenodo.7153326 ). This dataset comprises 400 multi-vendor MRI cases with high variability in stroke lesion size, quantity and location. It is split into a training dataset of n = 250 and a test dataset of n = 150. All training data is publicly available. The test dataset will be used for model validation only and will not be released to the public. This dataset serves as the foundation of the ISLES 2022 challenge ( https://www.isles-challenge.org/ ) with the goal of finding algorithmic methods to enable the development and benchmarking of automatic, robust and accurate segmentation methods for ischemic stroke.