The Diagnosis of Chronic Myeloid Leukemia with Deep Adversarial Learning

Chronic myeloid leukemia (CML) is a clonal proliferative disorder of granulocytic lineage, with morphologic evaluation as the first step for a definite diagnosis. This study developed a conditional generative adversarial network (cGAN)-based model, CMLcGAN, to segment megakaryocytes from myeloid cells in bone marrow biopsies. After segmentation, the statistical characteristics of two types of cells were extracted and compared between patients and controls. At the segmentation phase, the CMLcGAN was evaluated on 517 images (512 x 512) which achieved a mean pixel accuracy of 95.1%, a mean intersection over union of 71.2%, and a mean Dice coefficient of 81.8%. In addition, the CMLcGAN was compared with seven other available deep learning-based segmentation models and achieved a better segmentation performance. At the clinical validation phase, a series of seven-dimensional statistical features from various cells were extracted. Using the t-test, five-dimensional features were selected as the clinical prediction feature set. Finally, the model iterated 100 times using threefold cross-validation on whole slide images (58 CML cases and 31 healthy cases), and the final best AUC was 84.93%. In conclusion, a CMLcGAN model was established for multiclass segmentation of bone marrow cells that performed better than other deep learning-based segmentation models.

Automated summary

In this study, a model called CMLcGAN was developed for segmenting bone marrow cells, which achieved better performance compared to other segmentation models. By extracting statistical features and conducting clinical validation, the model demonstrated good accuracy in clinical prediction.