Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and the Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring and management. Here, we present the Stromata Contour Detection Algorithm version 2 (SCDA v2), which addresses the limitations of the previously developed SCDA version 1 (SCDA v1), without the need to empirically search for optimal decision-making input parameters (DMIPs) while achieving higher and consistent accuracy in tar spot stromata detection. SCDA v2 operates in two components: (i) SCDA v1 producing tar spot-like region proposals for a given input corn leaf Red-Green-Blue (RGB) image and (ii) a pretrained convolutional neural network (CNN) classifier identifying true tar spot stromata from the region proposals.

Bladder pheochromocytoma: case presentation and the use of OctreoScan for localization of extra-adrenal tumor sites in a pediatric patient.

An eleven year old boy presented with headaches and dizziness associated to micturition. On radiologic imaging, he was found with a bladder mass. The biochemical work up was suggestive of pheochromocytoma.