Spontaneous Unilateral Intrasphenoidal Meningocele.

The sphenoid sinus is an uncommon location for protrusion of a meningocele. When this does occur, it nearly always presents with leakage of cerebrospinal fluid through the nasal cavity. We present a case of a 38-year-old female found to have a meningocele protruding into the left sphenoid sinus, who presented with intractable headache but no CSF rhinorrhea. The lesion was discovered on computed tomography angiography, which was performed in order to rule out intracranial pathology as the etiology of her headache. Prior imaging, including pre- and post-contrast MRI, demonstrated the fluid within the sphenoid sinus, but did not reveal the communication through a defect in the base of the skull. Thus, it was assumed to be strictly related to sinus disease in the past. Our case represents a phenomenon whereby meningoceles protruding through the basilar skull into the sphenoid sinus or any other location are potentially misdiagnosed due to poor visualization of the osseous defect and lack of awareness of this entity.

Computer-aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level-set algorithms.

PURPOSE: Computerized liver extraction from hepatic CT images is challenging because the liver often abuts other organs of a similar density. The purpose of this study was to develop a computer-aided measurement of liver volumes in hepatic CT. METHODS: The authors developed a computerized liver extraction scheme based on geodesic active contour segmentation coupled with level-set contour evolution. First, an anisotropic diffusion filter was applied to portal-venous-phase CT images for noise reduction while preserving the liver structure, followed by a scale-specific gradient magnitude filter to enhance the liver boundaries. Then, a nonlinear grayscale converter enhanced the contrast of the liver parenchyma. By using the liver-parenchyma-enhanced image as a speed function, a fast-marching level-set algorithm generated an initial contour that roughly estimated the liver shape. A geodesic active contour segmentation algorithm coupled with level-set contour evolution refined the initial contour to define the liver boundaries more precisely. The liver volume was then calculated using these refined boundaries. Hepatic CT scans of 15 prospective liver donors were obtained under a liver transplant protocol with a multidetector CT system. The liver volumes extracted by the computerized scheme were compared to those traced manually by a radiologist, used as "gold standard." RESULTS: The mean liver volume obtained with our scheme was 1504 cc, whereas the mean gold standard manual volume was 1457 cc, resulting in a mean absolute difference of 105 cc (7.2%). The computer-estimated liver volumetrics agreed excellently with the gold-standard manual volumetrics (intraclass correlation coefficient was 0.95) with no statistically significant difference (F = 0.77; p(F < or = f) = 0.32). The average accuracy, sensitivity, specificity, and percent volume error were 98.4%, 91.1%, 99.1%, and 7.2%, respectively. Computerized CT liver volumetry would require substantially less completion time (compared to an average of 39 min per case by manual segmentation). CONCLUSIONS: The computerized liver extraction scheme provides an efficient and accurate way of measuring liver volumes in CT.