Acute Tentorial Subdural Hematoma Caused by Rupture of the Posterior Cerebral Artery after Minor Trauma—A Case Report

Acute subdural hematoma (aSDH) is a common pathology encountered after head trauma. Only a minority of aSDHs have an arterial source. In this article, we report a case of aSDH originating from a traumatic pseudoaneurysm of the distal segment of posterior cerebral artery (PCA), diagnosed several days after the initial minor trauma and successfully treated with endovascular coiling. This case emphasizes the importance of searching for vascular pathology when the localization, severity or relapsing course of the intracranial hemorrhage does not fully correspond to the severity of initial trauma and when the bleeding has a delayed onset. Characteristics, diagnostics and treatment possibilities of traumatic cerebral aneurysms, an important cause of arterial aSDH, are described in the article

Reinforcement learning-based anatomical maps for pancreas subregion and duct segmentation

AbstractBackground: The pancreas is a complex abdominal organ with many anatom-ical variations, and therefore automated pancreas segmentation from medicalimages is a challenging application.Purpose: In this paper, we present a framework for segmenting individualpancreatic subregions and the pancreatic duct from three-dimensional (3D)computed tomography (CT) images.Methods: A multiagent reinforcement learning (RL) network was used to detectlandmarks of the head,neck,body,and tail of the pancreas,and landmarks alongthe pancreatic duct in a selected target CT image. Using the landmark detectionresults, an atlas of pancreases was nonrigidly registered to the target image,resulting in anatomical probability maps for the pancreatic subregions and duct.The probability maps were augmented with multilabel 3D U-Net architecturesto obtain the final segmentation results.Results: To evaluate the performance of our proposed framework, we com-puted the Dice similarity coefficient (DSC) between the predicted and groundtruth manual segmentations on a database of 82 CT images with manuallysegmented pancreatic subregions and 37 CT images with manually segmentedpancreatic ducts. For the four pancreatic subregions, the mean DSC improvedfrom 0.38, 0.44, and 0.39 with standard 3D U-Net, Attention U-Net, and shiftedwindowing (Swin) U-Net architectures, to 0.51, 0.47, and 0.49, respectively, whenutilizing the proposed RL-based framework. For the pancreatic duct, the RL-based framework achieved a mean DSC of 0.70, significantly outperformingthe standard approaches and existing methods on different datasets.Conclusions: The resulting accuracy of the proposed RL-based segmentationframework demonstrates an improvement against segmentation with standardU-Net architectures