Image-based modeling and scoring of Howell-Jolly Bodies in human erythrocytes.

The spleen selectively removes cells with intracellular inclusions, for example, detached nuclear fragments in circulating erythrocytes, called Howell-Jolly Bodies (HJBs). With absent or deficient splenic function HJBs appear in the peripheral blood and can be used as a simple and non-invasive risk-indicator for fulminant potentially life-threatening infection after spleenectomy. However, it is still under debate whether counting of the rare HJBs is a reliable measure of splenic function. Investigating HJBs in premature erythrocytes from patients during radioiodine therapy gives about 10 thousand times higher HJB counts than in blood smears. However, we show that there is still the risk of false-positive results by unspecific nuclear remnants in the prepared samples that do not originate from HJBs, but from cell debris residing above or below the cell. Therefore, we present a method to improve accuracy of image-based tests that can be performed even in non-specialized medical institutions. We show how to selectively label HJB-like clusters in human blood samples and how to only count those that are undoubtedly inside the cell. We found a "critical distance" dcrit referring to a relative HJB-Cell distance that true HJBs do not exceed. To rule out false-positive counts we present a simple inside-outside-rule based on dcrit -a robust threshold that can be easily assessed by combining conventional 2D imaging and straight-forward image analysis. Besides data based on fluorescence imaging, simulations of randomly distributed HJB-like objects on realistically modelled cell objects demonstrate the risk and impact of biased counting in conventional analysis. © 2017 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.

Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images.

Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.