BackgroundLittle detailed knowledge is available regarding the etiology and outcome of CNS infection, particularly in HIV-infected individuals, in low-resource settings.MethodsFrom January 2015 to April 2016, we prospectively included all adults with suspected CNS infection in a referral hospital in Jakarta, Indonesia. Systematic screening included HIV testing, CSF examination, and neuroimaging.ResultsA total of 274 patients with suspected CNS infection (median age 26 years) presented after a median of 14 days with headache (77%), fever (78%), seizures (27%), or loss of consciousness (71%). HIV coinfection was common (54%), mostly newly diagnosed (30%) and advanced (median CD4 cell count 30/µL). Diagnosis was established in 167 participants (65%), including definite tuberculous meningitis (TBM) (n = 44), probable TBM (n = 48), cerebral toxoplasmosis (n = 48), cryptococcal meningitis (n = 14), herpes simplex virus/varicella-zoster virus/cytomegalovirus encephalitis (n = 10), cerebral lymphoma (n = 1), neurosyphilis (n = 1), and mucormycosis (n = 1). In-hospital mortality was 32%; 6-month mortality was 57%. The remaining survivors had either moderate or severe disability (36%) according to Glasgow Outcome Scale.ConclusionIn this setting, patients with CNS infections present late with severe disease and often associated with advanced HIV infection. Tuberculosis, toxoplasmosis, and cryptococcosis are common. High mortality and long-term morbidity underline the need for service improvements and further study.

Lumbar Spinal Stenosis causes low back pain through pressures exerted on the spinal nerves. This can be verified by measuring the anteroposterior diameter and foraminal widths of the patient’s lumbar spine. Our goal is to develop a novel strategy for assessing the extent of Lumbar Spinal Stenosis by automatically calculating these distances from the patient’s lumbar spine MRI. Our method starts with a semantic segmentation of T1- and T2-weighted composite axial MRI images using SegNet that partitions the image into six regions of interest. They consist of three main regions-of-interest, namely the Intervertebral Disc, Posterior Element, and Thecal Sac, and three auxiliary regions-of-interest that includes the Area between Anterior and Posterior elements. A novel contour evolution algorithm is then applied to improve the accuracy of the segmentation results along important region boundaries. Nine anatomical landmarks on the image are located by delineating the region boundaries found in the segmented image before the anteroposterior diameter and foraminal widths can be measured. The performance of the proposed algorithm was evaluated through a set of experiments on the Lumbar Spine MRI dataset containing MRI studies of 515 patients. These experiments compare the performance of our contour evolution algorithm with the Geodesic Active Contour and Chan-Vese methods over 22 different setups. We found that our method works best when our contour evolution algorithm is applied to improve the accuracy of both the label images used to train the SegNet model and the automatically segmented image. The average error of the calculated right and left foraminal distances relative to their expert-measured distances are 0.28 mm ( p = 0.92) and 0.29 mm ( p = 0.97), respectively. The average error of the calculated anteroposterior diameter relative to their expert-measured diameter is 0.90 mm ( p = 0.92). The method also achieves 96.7% agreement with an expert opinion on determining the severity of the Intervertebral Disc herniations.