Liu Jie, Yuan Ruize, Li Yinhao, Zhou Lin, Zhang Zhiqiang, Yang Jidong, Xiao Li
Department of Laboratory, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing, China.
Ann Transl Med. 2022 Feb;10(4):208. doi: 10.21037/atm-22-486.
Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.
In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.
We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.
Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.
骨髓细胞形态学分析被认为是白血病诊断的金标准。然而，由于骨髓细胞形态多样，形态学检查需要丰富的经验和耐心。迫切需要通过综合应用图像分析和模式识别技术的自动诊断系统来降低工作强度、错误概率并提高工作效率。
在本文中，我们基于深度学习目标检测框架建立了一种新的骨髓细胞检测形态学诊断系统。该模型基于经典目标检测模型更快区域卷积神经网络（R-CNN）。该系统自动检测骨髓细胞并确定其类型。由于标本具有严重的长尾分布，即不同类型细胞的频率差异很大，我们提出了一种通用分数排序损失来解决此类问题。通用分数排序损失考虑正负样本之间的排序关系，并对具有较高分类概率值的正样本进行优化。
我们用70例白血病患者的骨髓标本验证了该系统，证明其能高效实现智能识别。该软件最终集成到显微镜系统中以构建增强现实系统。
临床试验表明，新开发的诊断系统的响应速度比训练有素的诊断专家更快。
