Performance Evaluation of the CellaVision DM96 System

Abstract

We evaluated the CellaVision DM96 (CellaVision AB, Lund, Sweden), an automated digital cell morphology and informatics system for peripheral blood smears. Technologists agreed with 82% of the instrument's preclassifications. Correlation coefficients between final results released from the CellaVision and results obtained by direct microscopy were 0.96 (all neutrophils), 0.94 (lymphocytes), 0.88 (segmented neutrophils), 0.73 (eosinophils), 0.69 (bands), and 0.67 (monocytes). After correction for statistically and clinically insignificant variations, the CellaVision DM96 had 95% sensitivity and 88% specificity for immature myeloid cells. It was 100% sensitive and 94% specific for blasts, and 100% sensitive and 97% specific for unusual WBCs and nucleated RBCs. Advantages of the CellaVision DM96 over direct microscopy include the ability to review slides from a remote location, consultation and quality control on a cell-by-cell basis, and potential labor savings.

Can automated blood film analysis replace the manual differential? An evaluation of the CellaVision DM96 automated image analysis system

“…The timing study clearly demonstrates that the DM96 differential is faster than the manual differential this is consistent with earlier findings where on average the manual differential took 1.3 min longer to perform than when the DM96 was used (Kratz et al. , 2006).…”

Automated morphological analysis of cells in body fluids by the digital microscopy system DM96

“…We extensively tested this system on its precision and accuracy in classifying all major peripheral white blood cell categories 3. Based on our findings and those of previous evaluations of the DM96,4 5 we decided to put the DM96 into practice in our central laboratory location.…”

Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake

“…For example, Cellarvision Diffmaster Octavia (Swolin et al, 2003) and Cellarvision DM96 (Kratz et al, 2005) identify potential WBCs by scanning a whole slide at a low magnification using specific characteristics of WBCs, such as color, size, and shape, and then take digital images at a high magnification. Pre-classification is then performed using only the cropped digital images without WBC segmentation.…”