骨髓
细胞学
直方图
血液学
病理
医学
人工智能
计算机科学
内科学
图像(数学)
作者
Rohollah Moosavi Tayebi,Youqing Mu,Taher Dehkharghanian,Catherine Ross,Monalisa Sur,Ronan Foley,Hamid R. Tizhoosh,Clinton J.V. Campbell
标识
DOI:10.1038/s43856-022-00107-6
摘要
Bone marrow cytology is required to make a hematological diagnosis, influencing critical clinical decision points in hematology. However, bone marrow cytology is tedious, limited to experienced reference centers and associated with inter-observer variability. This may lead to a delayed or incorrect diagnosis, leaving an unmet need for innovative supporting technologies.We develop an end-to-end deep learning-based system for automated bone marrow cytology. Starting with a bone marrow aspirate digital whole slide image, our system rapidly and automatically detects suitable regions for cytology, and subsequently identifies and classifies all bone marrow cells in each region. This collective cytomorphological information is captured in a representation called Histogram of Cell Types (HCT) quantifying bone marrow cell class probability distribution and acting as a cytological patient fingerprint.Our system achieves high accuracy in region detection (0.97 accuracy and 0.99 ROC AUC), and cell detection and cell classification (0.75 mean average precision, 0.78 average F1-score, Log-average miss rate of 0.31).HCT has potential to eventually support more efficient and accurate diagnosis in hematology, supporting AI-enabled computational pathology.
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