Analysis of the blood cell distribution in bone marrow is necessary for a detailed diagnosis of many hematopoietic diseases, such as leukemia. While this task is performed manually on microscope images in clinical routine, automating it could improve reliability and objectivity. Cell detection tasks in medical imaging have successfully been solved using deep learning, in particular with RetinaNet, a powerful network architecture that yields good detection results in this scenario. It utilizes axis-parallel, rectangular bounding boxes to describe an object's position and size. However, since cells are mostly circular, this is suboptimal. We replace RetinaNet's anchors with more suitable Circular Anchors, which cover the shape of cells more precisely. We further introduce an extension to the Non-maximum Suppression algorithm that copes with predictions that differ in size. Experiments on hematopoietic cells in bone marrow images show that these methods reduce the number of false positive predictions and increase detection accuracy.

The aim of the present study was to analyze the parametric values of Androctonus crassicauda (Oliver, 1807), the most significant scorpion species in Turkey. For this purpose, 11 male and 11 female A. crassicauda specimens were collected from Sanliurfa region. The body parts most important for species identification were measured by using sensitive calipers. Differences between sexes were statistically significant for pectinal organ length (female: 7.50±1.26mm;male: 8.63±1.05mm; p=0.033), pectinal tooth count (female: 25.36±0.86;male: 33.36±0.80; p=0.000), and carapace width (female: 10.27±1.19mm;male: 9.09±0.73mm; p=0.000). However, body length (female: 90.09±14.74mm;male: 82.95±5.40mm), abdomen width (female: 12.09±1.90mm;male: 11.09±0.88mm), and metasoma length (female: 43.09±3.08mm;male: 44.63±6.50mm) were not significantly different between sexes