A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cells With Convolutional Neural Network

Context.—The rapid recognition of fetal nucleated red blood cells (fNRBCs) presents considerable challenges. Objective.—To establish a computer-aided diagnosis system for rapid recognition of fNRBCs by convolutional neural network. Design.—We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. The cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin staining. Then, we proposed a convolutional neural network–based, computer-aided diagnosis system to automatically discriminate features and recognize fNRBCs. Extracting methods of interested region were used to automatically segment individual cells in cell slices. The discriminant information from cellular-level regions of interest was encoded into a feature vector. Pathologic diagnoses were also provided by the network. Results.—In total, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures), the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5%, respectively, in the test set (1040 pictures). Conclusions.—We established a computer-aided diagnosis system for effective and accurate fNRBC recognition based on a convolutional neural network.