Soft counting networks for bone marrow differentials

Differential white cell counts from bone marrow preparations are very useful in evaluation of various hematologic disorders. It is tedious to locate, identify, and count these classes of cells, even by skilled hands. Automation of classification and counting would be of great benefit. However, the class structure of bone marrow or peripheral blood cells is not discrete; it represents a biological continuum of maturation levels. Because of this, there is uncertainty and overlap in characteristics of adjacent cell classes such that traditional pattern recognition techniques have difficulty in arriving at accurate cell counts. In this paper, we investigate soft counting networks that are trained to produce accurate overall class counts by allowing cells to have degrees of membership in multiple cell classes. This approach is applied to a bone marrow cell library and is compared with other standard recognition algorithms.