Bone marrow cellularity is an important measure in diagnostic hematopathology. Currently, the gold standard for bone marrow cellularity estimation is manual inspection of hematoxylin and eosin stained whole slide images (H&E WSI) by hematopathologists. However, these assessments are subjective and subject to interobserver and intraobserver variability. This may be reduced by using a computer-assisted estimate of bone marrow cellularity. The aim of this study was to develop a fully automated algorithm to estimate bone marrow cellularity in H&E WSI stains using bone marrow segmentation. Data consisted of eight bone marrow H&E WSIs extracted from eight subjects. An algorithm was developed to estimate the bone marrow cellularity consisting of biopsy segmentation, tissue classification, and bone marrow segmentation. Segmentations of the red and yellow bone marrow (YBM) were used to estimate the bone marrow cellularity within the WSI H&E stains. The DICE coefficient between automatic tissue segmentations and ground truth segmentations conducted by an experienced hematopathologist were used for validation. Furthermore, the agreement between the automatic and two manual cellularity estimates was assessed using Bland-Altman plots and intraclass correlation coefficients (ICC). The validation of the bone marrow segmentation demonstrated an average DICE of 0.901 and 0.920 for the red and YBM, respectively. A mean cellularity estimate difference of -0.552 and - 7.816 was obtained between the automatic cellularity estimates and two manual cellularity estimates, respectively. An ICC of 0.980 (95%CI: 0.925-0.995, P-value: 5.51 × 10-7 ) was obtained between the automatic and manual cellularity estimates based on manual annotations. The study demonstrated that it was possible to obtain bone marrow cellularity estimates with a good agreement with bone marrow cellularity estimates obtained from an experienced hematopathologist. © 2019 International Society for Advancement of Cytometry.

中文翻译：

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H＆E染色的完整玻片图像中的骨髓细胞自动估计。

骨髓细胞性是诊断血液病理学的重要指标。目前，骨髓细胞估计的金标准是由血液病理学家手动检查苏木精和曙红染色的全玻片图像（H＆E WSI）。但是，这些评估是主观的，并且受观察者之间和观察者内部差异的影响。这可以通过使用计算机辅助的骨髓细胞密度估计来减少。这项研究的目的是开发一种全自动算法，使用骨髓分割来评估H＆E WSI染色中的骨髓细胞。数据包括从八个受试者中提取的八个骨髓H＆E WSI。开发了一种算法来评估包括活检切片，组织分类和骨髓切片在内的骨髓细胞数量。红色和黄色骨髓（YBM）的分割用于估计WSI H＆E染色剂中的骨髓细胞。由经验丰富的血液病理学家进行的自动组织分割和地面真相分割之间的DICE系数用于验证。此外，使用Bland-Altman图和类内相关系数（ICC）来评估自动和两个手动细胞密度估计之间的一致性。骨髓分割的验证显示红色和YBM的平均DICE分别为0.901和0.920。自动蜂窝度估计和两个手动蜂窝度估计之间分别获得了-0.552和-7.816的平均蜂窝度估计差。ICC为0.980（95％CI：0.925-0.995，P值：5。在基于手动注释的自动和手动蜂窝度估计之间获得了51×10-7）。该研究表明，与从经验丰富的血液病理学家那里获得的骨髓细胞密度估计值有很好的一致性，可以获得骨髓细胞密度估计值。©2019国际细胞计数学会。