Multi-class semantic cell segmentation and classification of aplasia in bone marrow histology images

Bone marrow biopsies play a central role in hematopathology for diagnosing a variety of diseases, staging lymphomas or performing follow-up progression. Tasks performed while examining biopsies include counting cells and estimating the ratio of various hematopoietic lineages. Inter- and intra-observer variability between hematopathologists in the outcome of these tasks has been shown to be significant, which could result in multiple pathologists diagnosing some patients differently. To that end, this paper presents a fully-convolutional neural network (FCNN) architecture to segment six classes in bone marrow trephine biopsies, which could assist hematopathologists in identifying and delineating cells, thus reducing inter- and intra-observer variability. Additionally, to show an application of the neural network to a clinically relevant task, the output of the network is used to train a classifier capable of distinguishing between normocellular and aplastic bone marrow. Results indicate the network is successfully capable of segmenting cells with an average detection rate of 83%. The classifier for distinguishing normocellular/aplastic bone marrow reaches an AUC of 0.990, showing that is capable of automatically identifying aplasia.