Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, there are many challenges to perform automated BM NDC analysis on whole-slide images (WSIs), including large dimensions of data to process, complicated cell types with subtle differences. To the authors best knowledge, this is the first study on fully automatic BM NDC using WSIs with 40x objective magnification, which can replace traditional manual counting relying on light microscopy via oil-immersion 100x objective lens with a total 1000x magnification. In this study, we develop an efficient and fully automatic hierarchical deep learning framework for BM NDC WSI analysis in seconds. The proposed hierarchical framework consists of (1) a deep learning model for rapid localization of BM particles and cellular trails generating regions of interest (ROI) for further analysis, (2) a patch-based deep learning model for cell identification of 16 cell types, including megakaryocytes, mitotic cells, and four stages of erythroblasts which have not been demonstrated in previous studies before, and (3) a fast stitching model for integrating patch-based results and producing final outputs. In evaluation, the proposed method is firstly tested on a dataset with a total of 12,426 annotated cells using cross validation, achieving high recall and accuracy of 0.905 ± 0.078 and 0.989 ± 0.006, respectively, and taking only 44 seconds to perform BM NDC analysis for a WSI. To further examine the generalizability of our model, we conduct an evaluation on the second independent dataset with a total of 3005 cells, and the results show that the proposed method also obtains high recall and accuracy of 0.842 and 0.988, respectively. In comparison with the existing small-image-based benchmark methods, the proposed method demonstrates superior performance in recall, accuracy and computational time.

骨髓 (BM) 检查是诊断和管理多种血液疾病的重要步骤。作为 BM 检查的一部分，BM 有核差异计数 (NDC) 分析拥有最基本和最关键的信息。然而，对全幻灯片图像 (WSI) 执行自动 BM NDC 分析存在许多挑战，包括要处理的大尺寸数据、具有细微差异的复杂细胞类型。据作者所知，这是首次使用具有 40 倍物镜放大倍率的 WSI 对全自动 BM NDC 进行的研究，它可以通过总放大倍数为 1000 倍的油浸 100 倍物镜取代依赖光学显微镜的传统手动计数。在这项研究中，我们开发了一个高效且全自动的分层深度学习框架，用于在几秒钟内进行 BM NDC WSI 分析。所提出的分层框架包括 (1) 用于快速定位 BM 粒子和生成感兴趣区域 (ROI) 以供进一步分析的细胞轨迹的深度学习模型，(2) 用于 16 种细胞类型的细胞识别的基于补丁的深度学习模型，包括巨核细胞、有丝分裂细胞和四个阶段的成红细胞，这些在以前的研究中没有得到证实，以及 (3) 用于整合基于补丁的结果并产生最终输出的快速拼接模型。在评估中，该方法首先使用交叉验证在总共 12,426 个带注释的单元格的数据集上进行测试，分别实现了 0.905 ± 0.078 和 0.989 ± 0.006 的高召回率和准确率，并且只需 44 秒即可执行 BM NDC 分析一个 WSI。为了进一步检验我们模型的普遍性，我们对共有 3005 个细胞的第二个独立数据集进行了评估，结果表明，所提出的方法也分别获得了 0.842 和 0.988 的高召回率和准确率。与现有的基于小图像的基准方法相比，该方法在召回率、准确性和计算时间方面表现出优异的性能。