A system for automated counting of fetal and maternal red blood cells in clinical KB test

Abstract

The Kleihauer-Betke test (KBT) is a widely used method for measuring fetal-maternal hemorrhage (FMH) in maternal care. In hospitals, KBT is performed by a certified technologist to count a minimum of 2,000 fetal and maternal red blood cells (RBCs) on a blood smear. Manual counting is inherently inconsistent and subjective. This paper presents a system for automated counting and distinguishing fetal and maternal RBCs on clinical KB slides. A custom-adapted hardware platform is used for KB slide scanning and image capturing. Spatial-color pixel classification with spectral clustering is proposed to separate overlapping cells. Optimal clustering number and total cell number are obtained through maximizing cluster validity index. To accurately identify fetal RBCs from maternal RBCs, multiple features including cell size, shape, gradient and saturation difference are used in supervised learning to generate feature vectors, to tackle cell color, shape and contrast variations across clinical KB slides. The results show that the automated system is capable of completing the counting of over 60,000 cells (vs. 2,000 by technologists) within 5 minutes (vs. 15 minutes by technologists). The counting results are highly accurate and correlate strongly with those from benchmarking flow cytometry measurement.